1. The main weakness of this paper is the experiments section. The results are presented only on CIFAR-10 dataset and do not consider many other datasets from Federated learning benchmarks (e.g., LEAF https://leaf.cmu.edu/). The authors should see relevant works like (FedProx https://arxiv.org/abs/1812.06127) and (FedMAX, https://arxiv.org/abs/2004.03657 (ECML 2020)) for details on different datasets and model types. If the experimental evaluation was comprehensive enough, this would be a very good paper (given the interesting and important problem it addresses). 2. One other thing (although this is not the main focus of this paper), the authors should provide comparisons between strategies that result in fast convergence (without sparsity) vs. sparse methods? For example, do non-sparse, fast convergence methods (like FedProx, FedMAX, and others) result in small enough number of epochs compared to sparse methods? Can the fast convergence methods be augmented with sparisity ideas successfully without resulting in significant loss of accuracy? Some discussion and possibly performance numbers are needed here.
1. The main weakness of this paper is the experiments section. The results are presented only on CIFAR-10 dataset and do not consider many other datasets from Federated learning benchmarks (e.g., LEAF https://leaf.cmu.edu/). The authors should see relevant works like (FedProx https://arxiv.org/abs/1812.06127) and (FedMAX, https://arxiv.org/abs/2004.03657 (ECML 2020)) for details on different datasets and model types. If the experimental evaluation was comprehensive enough, this would be a very good paper (given the interesting and important problem it addresses).