Title: Fed-CBS: Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction. Abstract: Due to the limited communication capacities of edge devices, most existing federated learning (FL) methods randomly select only a subset of devices to participate in training for each communication round.
Compared with engaging all the available clients, the random-selection mechanism can lead to significant performance degradation on non-IID (independent and identically distributed) data.
In this paper, we show our key observation that the essential reason resulting in such performance degradation is the class-imbalance of the grouped data from the randomly selected clients.
Based on our key observation, we design an efficient heterogeneity-aware client sampling mechanism, i.e., Federated Class-balanced Sampling (Fed-CBS), which can effectively reduce class-imbalance of the group data from the intentionally selected clients. In particular, we propose a measure of class-imbalance and then employ homomorphic encryption to derive this measure in a privacy-preserving way. Based on this measure, we also design a computation-efficient client sampling strategy, such that the actively selected clients will generate a more class-balanced grouped dataset.
Extensive experimental results demonstrate Fed-CBS outperforms the status quo approaches. Furthermore, it achieves comparable or even better performance than the ideal setting where all the available clients participate in the FL training. In addition, we provide the theoretical convergence guarantee of Fed-CBS.