\begin{table}
\centering

\begin{adjustbox}{width=8.8cm,  center}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|} 
\hline
\multirow{3}{*}{\textbf{ Dataset}} & \multirow{3}{*}{\textbf{ Strategy}} & \multicolumn{10}{c|}{\textbf{Ratio of Stragglers}}                                                                                                                                          \\ 
\cline{3-12}
                                   &                                     & \multicolumn{2}{c|}{\textbf{Baseline}} & \multicolumn{2}{c|}{\textbf{10\%}} & \multicolumn{2}{c|}{\textbf{30\%}} & \multicolumn{2}{c|}{\textbf{50\%}} & \multicolumn{2}{c|}{\textbf{70\%}}  \\ 
\cline{3-12}
                                   &                                     & \textbf{Acc}   & \textbf{EUR}          & \textbf{Acc}   & \textbf{EUR}      & \textbf{Acc}   & \textbf{EUR}      & \textbf{Acc}   & \textbf{EUR}      & \textbf{Acc}   & \textbf{EUR}       \\ 
\hline
\multirow{3}{*}{MNIST}             & FedAvg                              & 0.981          & 0.99                  & \textbf{0.983} & 0.89              & \textbf{0.972} & 0.70               & 0.971          & 0.49              & 0.977          & 0.31               \\ 
\cline{2-12}
                                   & FedProx                             & 0.935          & 0.98                  & 0.972          & 0.88              & 0.960           & 0.69              & 0.962          & 0.49              & 0.967          & 0.29               \\ 
\cline{2-12}
                                   & FedLesScan                          & \textbf{0.985} & \textbf{0.99}         & 0.976          & \textbf{0.98}     & \textbf{0.972} & \textbf{0.96}     & \textbf{0.974} & \textbf{0.74}     & \textbf{0.979}  & \textbf{0.44}      \\ 
\hline
\multirow{3}{*}{FEMNIST}           & FedAvg                              & 0.756          & 0.99                  & 0.765          & 0.89              & 0.790           & 0.69              & 0.779         & 0.49              & 0.744           & 0.3                \\ 
\cline{2-12}
                                   & FedProx                             & 0.753          & 0.96                  & \textbf{0.778} & 0.80              & 0.756          & 0.64              & \textbf{0.785}  & 0.46              & 0.731           & 0.28               \\ 
\cline{2-12}
                                   & FedLesScan                          & \textbf{0.776} & \textbf{0.99}         & 0.770          & \textbf{0.97}     & \textbf{0.785} & \textbf{0.93}     & 0.774          & \textbf{0.80}      & \textbf{0.753} & \textbf{0.50}       \\ 
\hline
\multirow{3}{*}{Shakespeare}       & FedAvg                              & \textbf{0.434} & 0.87                  & \textbf{0.430}  & 0.80              & \textbf{0.401}   & 0.60               & 0.402   & 0.41              & 0.428           & 0.31               \\ 
\cline{2-12}
                                   & FedProx                             & 0.400          & 0.86                  & 0.399          & 0.78              & 0.396          & 0.58              & 0.345          & 0.40               & 0.38           & 0.29               \\ 
\cline{2-12}
                                   & FedLesScan                          & 0.388          & \textbf{0.94}         & 0.408          & \textbf{0.90}      & 0.400          & \textbf{0.86}     & \textbf{0.403}   & \textbf{0.72}     & \textbf{0.432}  & \textbf{0.53}      \\ 
\hline
\multirow{3}{*}{Google Speech}     & FedAvg                              & 0.766           & \textbf{0.99}        & 0.699            & 0.90               & 0.639           & 0.70               & 0.645           & 0.50               & 0.804           & 0.30                \\ 
\cline{2-12}
                                   & FedProx                             & 0.774          & \textbf{0.99}         & 0.664           & 0.89              & 0.709           & 0.70               & 0.694           & 0.49              & 0.759           & 0.29               \\ 
\cline{2-12}
                                   & FedLesScan                          & \textbf{0.794} & \textbf{0.99}         & \textbf{0.762}   & \textbf{0.97}     & \textbf{0.719}  & \textbf{0.90}      & \textbf{0.720}  & \textbf{0.86}     & \textbf{0.824}  & \textbf{0.74}      \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Accuracy and EUR values for the three strategies across different scenarios (\S\ref{sec:scenarios}) and datasets (\S\ref{sec:datasets}). The highlighted values represent the highest accuracy and EUR values for a particular dataset, strategy and scenario.}
\label{tab:acc-all}
\vspace{-5mm}
\vspace{-1mm}
\end{table}
The different proposed approaches for straggler mitigation in FL can be divided into three categories, i.e., \textit{synchronous}~\cite{fedprox}, \textit{asynchronous}~\cite{asofed,fed_async}, and \textit{semi-asynchronous}~\cite{fedAt, safa, csafl}. One of the most popular synchronous approaches is \texttt{FedProx}~\cite{fedprox}, which is based on \texttt{FedAvg}~\cite{mcmahan2017communication} with two minor differences. First, a custom loss function for client-side training that limits the varying effects of local model updates. Second, toleration for partial work, i.e., clients can perform a variable amount of work to accommodate constraints in terms of hardware and network. However, incorporating partial work requires tailoring the number of local epochs for each client individually, which might be infeasible for a significantly large number of clients. Moreover, in contrast to \texttt{FedLesScan} it relies on random selection of clients which makes it sensitive to stragglers. In~\cite{fed_async}, Xie et al. propose an asynchronous federated optimization algorithm called \texttt{FedAsync} that relies on a parameter server architecture to invoke and synchronize FL clients. The server consists of two parallel threads, i.e., a \textit{scheduler} and an \textit{updater}. The scheduler thread periodically triggers the FL clients to perform training using the latest global model, while the updater thread receives the client model updates and directly aggregates them to the global
model. However, in the context of serverless, this approach has significantly higher resource and communication costs (\S\ref{sec:faas}, \S\ref{sec:servfl}) since it would require a function invocation to perform aggregation with the global model after each individual client update.

\section{Experimental Results}
\label{sec:results}
In this section, we first describe the datasets and model architectures used for evaluating our strategy. Following this, we describe the configuration for \emph{FedLess} and the FaaS client functions. Finally, we evaluate the performance of our strategy as compared to \texttt{FedAvg}~\cite{mcmahan2017communication} and \texttt{FedProx}~\cite{fedprox} wrt several metrics for varying number of stragglers. We chose these strategies since they are robust and work well in a serverless setting. For all our experiments, we follow best practices while reporting results and repeat them three times~\cite{8758926}. More extensive experimental results can be found here~\cite{thesismohamed}.

\textbf{Straggler (\%) Scenario}. In this, we simulate varying percentages of stragglers in the FL system.  Although there might be different reasons for FaaS client failures, such as memory limit, function timeout, or communication issues, these failures can only have one of two effects on the clients. Clients can either push their updates after the training round is finished (slow updates) or can completely crash (not push their updates). To simulate slow updates, we limit the training round time to only fit clients with no issues or delays. Meaning, that clients which experience cold starts, bandwidth limitations, or communication delays do not finish the round in time; therefore, pushing their updates later. On the other hand, to simulate failures, we randomly select a specific ratio of clients to fail their training at the beginning of each experiment. We perform four different experiments for each dataset with 10\%, 30\%, 50\%, and 70\% stragglers in the system. Note that, in all our experiments, stragglers are different from \textit{malicious clients} found in some FL settings~\cite{li2020learning, zhang2022fldetector}. While malicious clients can act as stragglers to deliberately slow training or hinder model performance, they can perform more powerful attacks such as data or model poisoning~\cite{fl_challenges,fl_data_poisoning} to skew the model's performance. In our work, we focus on the problem of stragglers and their behavior rather than the clients' intentions. The number of training rounds for the different datasets and experiment scenarios is shown in Table~\ref{tab:experiment-params}.

\subsubsection{Metrics}
\label{sec:metrics}
For comparing the different strategies, we use the standard metrics \textit{accuracy}, \textit{experiment duration}, and \textit{cost}. For calculating accuracy of the trained global model, we randomly choose a set of clients and evaluate it on their test datasets. Following this, we multiply the obtained accuracy for a particular client by the ratio of its test set cardinality to the total cardinality of the test dataset. The final accuracy value is obtained by averaging the obtained accuracy values. While accuracy describes model performance, it does not provide insights about the performance of the strategy and the contributions of the clients to the global model. Towards this, we also use the metrics \textit{Effective Update Ratio} (EUR)~\cite{safa} and \textit{Bias}~\cite{safa}. EUR is defined as the ratio between the successful clients and the subset of selected clients. It shows the effect of stragglers on round utilization. A higher value of EUR represents less wasted resources since clients requested to participate in a certain round end up contributing to the global model. Furthermore, to provide insights into the bias of the client selection schemes, we use variance plots. This is done by showing the frequency of selection for each client across the FL training process. Bias is defined as the difference between the frequency of the least called client and the most called client~\cite{safa}. For scenarios with low number of stragglers, we target low bias, while for scenarios with high number of stragglers the bias should be higher due to the prioritization of reliable clients in training. Experiment duration represents the total time required for training the model. For computing training costs, we use the cost computation model~\cite{gcf_cost} used by Google to estimate the cost for each client function based on the number of invocations, allocated memory, and execution duration.

The obtained accuracy and EUR values across all our experiments is summarized in Table~\ref{tab:acc-all}. For the \textit{standard scenarios} (\S\ref{sec:scenarios}), we obtained better accuracy and round utlization for our strategy as compared to \texttt{FedAvg} and \texttt{FedProx} across all datasets except Shakespeare. This is because with the Shakespeare dataset, some of the clients contribute to the model accuracy more than others, especially clients with longer training times. Moreover, due to budget constraints and significantly high training costs, we train for a slightly less number of rounds, i.e., 25 for Shakespeare. However, we argue that in a more realistic scenario with more number of rounds, the difference in accuracy will decrease. This is because our strategy utilizes clients more efficiently, i.e., higher EUR. As a result, with more rounds the number of invocations per client will increase and more clients will contribute to the global model, leading to a higher accuracy. Similarly, for the \textit{straggler (\%) scenarios}, we obtained better results for accuracy with our strategy as compared to the other two for the MNIST, FEMNIST, and the Google Speech datasets. On the Shakespeare dataset, our strategy outperformed \texttt{FedAvg} and \texttt{FedProx} in scenarios with 30\%, 50\%, and 70\% stragglers. In terms of EUR, our strategy constantly outperforms the other two strategies across all scenarios and datasets since they use random selection for selecting clients for an FL training round.

For most \textit{standard scenarios} (Table~\ref{tab:acc-all}), \texttt{FedLesScan} obtained better accuracy as compared to the other two strategies due to the better distribution of client invocations. This is because our strategy prioritizes clients with the least number of invocations in a selected client cluster (\S\ref{sec:ClientSelection}) leading to more balanced contributions among the participating clients. On the other hand, with \textit{straggler (\%) scenario}, our strategy reached better accuracies by relying more on robust and reliable clients. Furthermore, the utilization of a staleness-aware aggregation scheme (\S\ref{sec:stalelessaware}) avoids wasting valuable contributions, which in turn increases accuracy.

To offer detailed insights, we present results for the Google Speech dataset~\cite{speech} wrt the metrics accuracy and EUR, across the FL training session as shown in Figures \ref{fig:acc-speech} and \ref{fig:eur-speech} respectively. For the \textit{standard scenarios}, we ran the FL training session for $35$ rounds, while for the \textit{straggler (\%) scenarios}, we ran the experiments for $60$ rounds. For the standard scenario, our strategy reached an accuracy of 79.4\% as compared to 76.6\% and 77.4\% for \texttt{FedAvg} and \texttt{FedProx} respectively. Furthermore, our strategy showed faster convergence by reaching an accuracy of 70\% in 19 rounds as compared to 21 and 22 for \texttt{FedAvg} and \texttt{FedProx} respectively. With 10\% stragglers, our strategy and \texttt{FedAvg} had a similar convergence rate, while \texttt{FedProx} was slightly behind. Moreover, our strategy reached an end accuracy of 76\% which is a 6\% and 10\% increase over \texttt{FedAvg} and \texttt{FedProx} respectively. For an FL system with 30\% stragglers, our strategy consistently outperforms \texttt{FedAvg} by around 8\% towards the end of the training, while outperforming \texttt{FedProx} by a smaller margin of 1\%. We observe a similar trend for our experiments with 50\% and 70\% stragglers in the system, where our strategy outperforms the other two. From our experiments, we observe that the presence of stragglers affects the convergence speed of the DNN models in an FL training session. DNN models trained in \textit{standard scenarios} converge faster as compared to the models trained with \textit{straggler (\%) scenarios} as shown in Figure~\ref{fig:acc-speech} (Table~\ref{tab:experiment-params}). We observe a similar behaviour for the different datasets (\S\ref{sec:datasets}). Moreover, for the \textit{straggler (\%) scenarios}, increasing the percentage of stragglers in the system does not consistently decrease the accuracy of the trained DNN model as shown in Table~\ref{tab:acc-all}. This is especially true for experiments with a higher number of stragglers, i.e., $70\%$. This behavior is not exclusive to our experiments and was also reported by Li et al.~\cite{fedprox} and Wu et al.~\cite{safa}. While the authors do not provide a clear explanation for this behaviour, we argue that due to the non-IID nature of the data, clients do not contribute evenly to the test accuracy. In addition, having fewer number of reliable clients reduces the varying effect of local model updates and local model deviations from the global model, making it easier to reach a consensus on the global model. Therefore, we can reach situations where a system with more stragglers reaches higher overall accuracy at the end. 
{"value": "10", "type": "Other"} {"value": "30", "type": "Other"} {"value": "50", "type": "Other"} {"value": "70", "type": "Other"} {"value": "0.981", "type": "Result", "training data/set": "MNIST", "test data/set": "MNIST", "task": "Image Classification", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "Baseline"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.99", "type": "Result", "training data/set": "MNIST", "test data/set": "MNIST", "task": "Image Classification", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "Baseline"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.983", "type": "Result", "training data/set": "MNIST", "test data/set": "MNIST", "task": "Image Classification", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "10%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.89", "type": "Result", "training data/set": "MNIST", "test data/set": "MNIST", "task": "Image Classification", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "10%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.972", "type": "Result", "training data/set": "MNIST", "test data/set": "MNIST", "task": "Image Classification", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "30%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.70", "type": "Result", "training data/set": "MNIST", "test data/set": "MNIST", "task": "Image Classification", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "30%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.971", "type": "Result", "training data/set": "MNIST", "test data/set": "MNIST", "task": "Image Classification", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "50%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.49", "type": "Result", "training data/set": "MNIST", "test data/set": "MNIST", "task": "Image Classification", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "50%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.977", "type": "Result", "training data/set": "MNIST", "test data/set": "MNIST", "task": "Image Classification", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "70%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.31", "type": "Result", "training data/set": "MNIST", "test data/set": "MNIST", "task": "Image Classification", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "70%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.935", "type": "Result", "training data/set": "MNIST", "test data/set": "MNIST", "task": "Image Classification", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "Baseline"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.98", "type": "Result", "training data/set": "MNIST", "test data/set": "MNIST", "task": "Image Classification", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "Baseline"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.972", "type": "Result", "training data/set": "MNIST", "test data/set": "MNIST", "task": "Image Classification", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "10%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.88", "type": "Result", "training data/set": "MNIST", "test data/set": "MNIST", "task": "Image Classification", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "10%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.960", "type": "Result", "training data/set": "MNIST", "test data/set": "MNIST", "task": "Image Classification", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "30%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.69", "type": "Result", "training data/set": "MNIST", "test data/set": "MNIST", "task": "Image Classification", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "30%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.962", "type": "Result", "training data/set": "MNIST", "test data/set": "MNIST", "task": "Image Classification", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "50%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.49", "type": "Result", "training data/set": "MNIST", "test data/set": "MNIST", "task": "Image Classification", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "50%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.967", "type": "Result", "training data/set": "MNIST", "test data/set": "MNIST", "task": "Image Classification", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "70%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.29", "type": "Result", "training data/set": "MNIST", "test data/set": "MNIST", "task": "Image Classification", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "70%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.985", "type": "Result", "training data/set": "MNIST", "test data/set": "MNIST", "task": "Image Classification", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "Baseline"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.99", "type": "Result", "training data/set": "MNIST", "test data/set": "MNIST", "task": "Image Classification", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "Baseline"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.976", "type": "Result", "training data/set": "MNIST", "test data/set": "MNIST", "task": "Image Classification", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "10%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.98", "type": "Result", "training data/set": "MNIST", "test data/set": "MNIST", "task": "Image Classification", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "10%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.972", "type": "Result", "training data/set": "MNIST", "test data/set": "MNIST", "task": "Image Classification", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "30%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.96", "type": "Result", "training data/set": "MNIST", "test data/set": "MNIST", "task": "Image Classification", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "30%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.974", "type": "Result", "training data/set": "MNIST", "test data/set": "MNIST", "task": "Image Classification", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "50%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.74", "type": "Result", "training data/set": "MNIST", "test data/set": "MNIST", "task": "Image Classification", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "50%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.979", "type": "Result", "training data/set": "MNIST", "test data/set": "MNIST", "task": "Image Classification", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "70%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.44", "type": "Result", "training data/set": "MNIST", "test data/set": "MNIST", "task": "Image Classification", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "70%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.756", "type": "Result", "training data/set": "FEMNIST", "test data/set": "FEMNIST", "task": "Image Classification", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "Baseline"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.99", "type": "Result", "training data/set": "FEMNIST", "test data/set": "FEMNIST", "task": "Image Classification", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "Baseline"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.765", "type": "Result", "training data/set": "FEMNIST", "test data/set": "FEMNIST", "task": "Image Classification", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "10%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.89", "type": "Result", "training data/set": "FEMNIST", "test data/set": "FEMNIST", "task": "Image Classification", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "10%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.790", "type": "Result", "training data/set": "FEMNIST", "test data/set": "FEMNIST", "task": "Image Classification", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "30%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.69", "type": "Result", "training data/set": "FEMNIST", "test data/set": "FEMNIST", "task": "Image Classification", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "30%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.779", "type": "Result", "training data/set": "FEMNIST", "test data/set": "FEMNIST", "task": "Image Classification", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "50%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.49", "type": "Result", "training data/set": "FEMNIST", "test data/set": "FEMNIST", "task": "Image Classification", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "50%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.744", "type": "Result", "training data/set": "FEMNIST", "test data/set": "FEMNIST", "task": "Image Classification", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "70%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.3", "type": "Result", "training data/set": "FEMNIST", "test data/set": "FEMNIST", "task": "Image Classification", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "70%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.753", "type": "Result", "training data/set": "FEMNIST", "test data/set": "FEMNIST", "task": "Image Classification", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "Baseline"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.96", "type": "Result", "training data/set": "FEMNIST", "test data/set": "FEMNIST", "task": "Image Classification", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "Baseline"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.778", "type": "Result", "training data/set": "FEMNIST", "test data/set": "FEMNIST", "task": "Image Classification", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "10%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.80", "type": "Result", "training data/set": "FEMNIST", "test data/set": "FEMNIST", "task": "Image Classification", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "10%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.756", "type": "Result", "training data/set": "FEMNIST", "test data/set": "FEMNIST", "task": "Image Classification", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "30%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.64", "type": "Result", "training data/set": "FEMNIST", "test data/set": "FEMNIST", "task": "Image Classification", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "30%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.785", "type": "Result", "training data/set": "FEMNIST", "test data/set": "FEMNIST", "task": "Image Classification", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "50%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.46", "type": "Result", "training data/set": "FEMNIST", "test data/set": "FEMNIST", "task": "Image Classification", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "50%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.731", "type": "Result", "training data/set": "FEMNIST", "test data/set": "FEMNIST", "task": "Image Classification", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "70%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.28", "type": "Result", "training data/set": "FEMNIST", "test data/set": "FEMNIST", "task": "Image Classification", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "70%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.776", "type": "Result", "training data/set": "FEMNIST", "test data/set": "FEMNIST", "task": "Image Classification", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "Baseline"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.99", "type": "Result", "training data/set": "FEMNIST", "test data/set": "FEMNIST", "task": "Image Classification", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "Baseline"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.770", "type": "Result", "training data/set": "FEMNIST", "test data/set": "FEMNIST", "task": "Image Classification", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "10%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.97", "type": "Result", "training data/set": "FEMNIST", "test data/set": "FEMNIST", "task": "Image Classification", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "10%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.785", "type": "Result", "training data/set": "FEMNIST", "test data/set": "FEMNIST", "task": "Image Classification", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "30%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.93", "type": "Result", "training data/set": "FEMNIST", "test data/set": "FEMNIST", "task": "Image Classification", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "30%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.774", "type": "Result", "training data/set": "FEMNIST", "test data/set": "FEMNIST", "task": "Image Classification", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "50%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.80", "type": "Result", "training data/set": "FEMNIST", "test data/set": "FEMNIST", "task": "Image Classification", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "50%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.753", "type": "Result", "training data/set": "FEMNIST", "test data/set": "FEMNIST", "task": "Image Classification", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "70%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.50", "type": "Result", "training data/set": "FEMNIST", "test data/set": "FEMNIST", "task": "Image Classification", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "70%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.434", "type": "Result", "training data/set": "Shakespeare", "test data/set": "Shakespeare", "task": "Language Modeling", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "Baseline"}, "model": ["LSTM", "Long Short Term Memory"], "model settings": {"xx": "yy"}} {"value": "0.87", "type": "Result", "training data/set": "Shakespeare", "test data/set": "Shakespeare", "task": "Language Modeling", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "Baseline"}, "model": ["LSTM", "Long Short Term Memory"], "model settings": {"xx": "yy"}} {"value": "0.430", "type": "Result", "training data/set": "Shakespeare", "test data/set": "Shakespeare", "task": "Language Modeling", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "10%"}, "model": ["LSTM", "Long Short Term Memory"], "model settings": {"xx": "yy"}} {"value": "0.80", "type": "Result", "training data/set": "Shakespeare", "test data/set": "Shakespeare", "task": "Language Modeling", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "10%"}, "model": ["LSTM", "Long Short Term Memory"], "model settings": {"xx": "yy"}} {"value": "0.401", "type": "Result", "training data/set": "Shakespeare", "test data/set": "Shakespeare", "task": "Language Modeling", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "30%"}, "model": ["LSTM", "Long Short Term Memory"], "model settings": {"xx": "yy"}} {"value": "0.60", "type": "Result", "training data/set": "Shakespeare", "test data/set": "Shakespeare", "task": "Language Modeling", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "30%"}, "model": ["LSTM", "Long Short Term Memory"], "model settings": {"xx": "yy"}} {"value": "0.402", "type": "Result", "training data/set": "Shakespeare", "test data/set": "Shakespeare", "task": "Language Modeling", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "50%"}, "model": ["LSTM", "Long Short Term Memory"], "model settings": {"xx": "yy"}} {"value": "0.41", "type": "Result", "training data/set": "Shakespeare", "test data/set": "Shakespeare", "task": "Language Modeling", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "50%"}, "model": ["LSTM", "Long Short Term Memory"], "model settings": {"xx": "yy"}} {"value": "0.428", "type": "Result", "training data/set": "Shakespeare", "test data/set": "Shakespeare", "task": "Language Modeling", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "70%"}, "model": ["LSTM", "Long Short Term Memory"], "model settings": {"xx": "yy"}} {"value": "0.31", "type": "Result", "training data/set": "Shakespeare", "test data/set": "Shakespeare", "task": "Language Modeling", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "70%"}, "model": ["LSTM", "Long Short Term Memory"], "model settings": {"xx": "yy"}} {"value": "0.400", "type": "Result", "training data/set": "Shakespeare", "test data/set": "Shakespeare", "task": "Language Modeling", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "Baseline"}, "model": ["LSTM", "Long Short Term Memory"], "model settings": {"xx": "yy"}} {"value": "0.86", "type": "Result", "training data/set": "Shakespeare", "test data/set": "Shakespeare", "task": "Language Modeling", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "Baseline"}, "model": ["LSTM", "Long Short Term Memory"], "model settings": {"xx": "yy"}} {"value": "0.399", "type": "Result", "training data/set": "Shakespeare", "test data/set": "Shakespeare", "task": "Language Modeling", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "10%"}, "model": ["LSTM", "Long Short Term Memory"], "model settings": {"xx": "yy"}} {"value": "0.78", "type": "Result", "training data/set": "Shakespeare", "test data/set": "Shakespeare", "task": "Language Modeling", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "10%"}, "model": ["LSTM", "Long Short Term Memory"], "model settings": {"xx": "yy"}} {"value": "0.396", "type": "Result", "training data/set": "Shakespeare", "test data/set": "Shakespeare", "task": "Language Modeling", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "30%"}, "model": ["LSTM", "Long Short Term Memory"], "model settings": {"xx": "yy"}} {"value": "0.58", "type": "Result", "training data/set": "Shakespeare", "test data/set": "Shakespeare", "task": "Language Modeling", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "30%"}, "model": ["LSTM", "Long Short Term Memory"], "model settings": {"xx": "yy"}} {"value": "0.345", "type": "Result", "training data/set": "Shakespeare", "test data/set": "Shakespeare", "task": "Language Modeling", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "50%"}, "model": ["LSTM", "Long Short Term Memory"], "model settings": {"xx": "yy"}} {"value": "0.40", "type": "Result", "training data/set": "Shakespeare", "test data/set": "Shakespeare", "task": "Language Modeling", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "50%"}, "model": ["LSTM", "Long Short Term Memory"], "model settings": {"xx": "yy"}} {"value": "0.38", "type": "Result", "training data/set": "Shakespeare", "test data/set": "Shakespeare", "task": "Language Modeling", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "70%"}, "model": ["LSTM", "Long Short Term Memory"], "model settings": {"xx": "yy"}} {"value": "0.29", "type": "Result", "training data/set": "Shakespeare", "test data/set": "Shakespeare", "task": "Language Modeling", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "70%"}, "model": ["LSTM", "Long Short Term Memory"], "model settings": {"xx": "yy"}} {"value": "0.388", "type": "Result", "training data/set": "Shakespeare", "test data/set": "Shakespeare", "task": "Language Modeling", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "Baseline"}, "model": ["LSTM", "Long Short Term Memory"], "model settings": {"xx": "yy"}} {"value": "0.94", "type": "Result", "training data/set": "Shakespeare", "test data/set": "Shakespeare", "task": "Language Modeling", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "Baseline"}, "model": ["LSTM", "Long Short Term Memory"], "model settings": {"xx": "yy"}} {"value": "0.408", "type": "Result", "training data/set": "Shakespeare", "test data/set": "Shakespeare", "task": "Language Modeling", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "10%"}, "model": ["LSTM", "Long Short Term Memory"], "model settings": {"xx": "yy"}} {"value": "0.90", "type": "Result", "training data/set": "Shakespeare", "test data/set": "Shakespeare", "task": "Language Modeling", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "10%"}, "model": ["LSTM", "Long Short Term Memory"], "model settings": {"xx": "yy"}} {"value": "0.400", "type": "Result", "training data/set": "Shakespeare", "test data/set": "Shakespeare", "task": "Language Modeling", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "30%"}, "model": ["LSTM", "Long Short Term Memory"], "model settings": {"xx": "yy"}} {"value": "0.86", "type": "Result", "training data/set": "Shakespeare", "test data/set": "Shakespeare", "task": "Language Modeling", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "30%"}, "model": ["LSTM", "Long Short Term Memory"], "model settings": {"xx": "yy"}} {"value": "0.403", "type": "Result", "training data/set": "Shakespeare", "test data/set": "Shakespeare", "task": "Language Modeling", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "50%"}, "model": ["LSTM", "Long Short Term Memory"], "model settings": {"xx": "yy"}} {"value": "0.72", "type": "Result", "training data/set": "Shakespeare", "test data/set": "Shakespeare", "task": "Language Modeling", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "50%"}, "model": ["LSTM", "Long Short Term Memory"], "model settings": {"xx": "yy"}} {"value": "0.432", "type": "Result", "training data/set": "Shakespeare", "test data/set": "Shakespeare", "task": "Language Modeling", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "70%"}, "model": ["LSTM", "Long Short Term Memory"], "model settings": {"xx": "yy"}} {"value": "0.53", "type": "Result", "training data/set": "Shakespeare", "test data/set": "Shakespeare", "task": "Language Modeling", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "70%"}, "model": ["LSTM", "Long Short Term Memory"], "model settings": {"xx": "yy"}} {"value": "0.766", "type": "Result", "training data/set": "Google Speech", "test data/set": "Google Speech", "task": "Speech Recognition", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "Baseline"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.99", "type": "Result", "training data/set": "Google Speech", "test data/set": "Google Speech", "task": "Speech Recognition", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "Baseline"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.699", "type": "Result", "training data/set": "Google Speech", "test data/set": "Google Speech", "task": "Speech Recognition", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "10%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.90", "type": "Result", "training data/set": "Google Speech", "test data/set": "Google Speech", "task": "Speech Recognition", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "10%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.639", "type": "Result", "training data/set": "Google Speech", "test data/set": "Google Speech", "task": "Speech Recognition", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "30%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.70", "type": "Result", "training data/set": "Google Speech", "test data/set": "Google Speech", "task": "Speech Recognition", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "30%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.645", "type": "Result", "training data/set": "Google Speech", "test data/set": "Google Speech", "task": "Speech Recognition", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "50%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.50", "type": "Result", "training data/set": "Google Speech", "test data/set": "Google Speech", "task": "Speech Recognition", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "50%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.804", "type": "Result", "training data/set": "Google Speech", "test data/set": "Google Speech", "task": "Speech Recognition", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "70%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.30", "type": "Result", "training data/set": "Google Speech", "test data/set": "Google Speech", "task": "Speech Recognition", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedAvg", "Ratio of Stragglers": "70%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.774", "type": "Result", "training data/set": "Google Speech", "test data/set": "Google Speech", "task": "Speech Recognition", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "Baseline"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.99", "type": "Result", "training data/set": "Google Speech", "test data/set": "Google Speech", "task": "Speech Recognition", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "Baseline"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.664", "type": "Result", "training data/set": "Google Speech", "test data/set": "Google Speech", "task": "Speech Recognition", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "10%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.89", "type": "Result", "training data/set": "Google Speech", "test data/set": "Google Speech", "task": "Speech Recognition", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "10%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.709", "type": "Result", "training data/set": "Google Speech", "test data/set": "Google Speech", "task": "Speech Recognition", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "30%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.70", "type": "Result", "training data/set": "Google Speech", "test data/set": "Google Speech", "task": "Speech Recognition", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "30%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.694", "type": "Result", "training data/set": "Google Speech", "test data/set": "Google Speech", "task": "Speech Recognition", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "50%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.49", "type": "Result", "training data/set": "Google Speech", "test data/set": "Google Speech", "task": "Speech Recognition", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "50%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.759", "type": "Result", "training data/set": "Google Speech", "test data/set": "Google Speech", "task": "Speech Recognition", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "70%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.29", "type": "Result", "training data/set": "Google Speech", "test data/set": "Google Speech", "task": "Speech Recognition", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedProx", "Ratio of Stragglers": "70%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.794", "type": "Result", "training data/set": "Google Speech", "test data/set": "Google Speech", "task": "Speech Recognition", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "Baseline"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.99", "type": "Result", "training data/set": "Google Speech", "test data/set": "Google Speech", "task": "Speech Recognition", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "Baseline"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.762", "type": "Result", "training data/set": "Google Speech", "test data/set": "Google Speech", "task": "Speech Recognition", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "10%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.97", "type": "Result", "training data/set": "Google Speech", "test data/set": "Google Speech", "task": "Speech Recognition", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "10%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.719", "type": "Result", "training data/set": "Google Speech", "test data/set": "Google Speech", "task": "Speech Recognition", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "30%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.90", "type": "Result", "training data/set": "Google Speech", "test data/set": "Google Speech", "task": "Speech Recognition", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "30%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.720", "type": "Result", "training data/set": "Google Speech", "test data/set": "Google Speech", "task": "Speech Recognition", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "50%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.86", "type": "Result", "training data/set": "Google Speech", "test data/set": "Google Speech", "task": "Speech Recognition", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "50%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.824", "type": "Result", "training data/set": "Google Speech", "test data/set": "Google Speech", "task": "Speech Recognition", "metric": ["Acc", "Accuracy"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "70%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}} {"value": "0.74", "type": "Result", "training data/set": "Google Speech", "test data/set": "Google Speech", "task": "Speech Recognition", "metric": ["EUR", "Effective Update Ratio"], "experimental settings": {"Strategy": "FedLesScan", "Ratio of Stragglers": "70%"}, "model": ["CNN", "Convolutional Neural Network"], "model settings": {"xx": "yy"}}