Learning Critically: Selective Self Distillation in Federated Learning on Non-IID Data
This addresses a key challenge in federated learning for decentralized data scenarios, offering an incremental improvement over existing methods.
The paper tackles the problem of data heterogeneity (non-IID) in federated learning, which causes performance degradation and slow convergence, by proposing FedSSD, a selective self-distillation method that achieves better generalization and robustness in fewer communication rounds on three benchmark datasets.
Federated learning (FL) enables multiple clients to collaboratively train a global model while keeping local data decentralized. Data heterogeneity (non-IID) across clients has imposed significant challenges to FL, which makes local models re-optimize towards their own local optima and forget the global knowledge, resulting in performance degradation and convergence slowdown. Many existing works have attempted to address the non-IID issue by adding an extra global-model-based regularizing item to the local training but without an adaption scheme, which is not efficient enough to achieve high performance with deep learning models. In this paper, we propose a Selective Self-Distillation method for Federated learning (FedSSD), which imposes adaptive constraints on the local updates by self-distilling the global model's knowledge and selectively weighting it by evaluating the credibility at both the class and sample level. The convergence guarantee of FedSSD is theoretically analyzed and extensive experiments are conducted on three public benchmark datasets, which demonstrates that FedSSD achieves better generalization and robustness in fewer communication rounds, compared with other state-of-the-art FL methods.