On the effectiveness of partial variance reduction in federated learning with heterogeneous data
This addresses performance issues in federated learning for deep neural networks with heterogeneous data, offering an incremental improvement over existing methods.
The paper tackles the challenge of data heterogeneity in federated learning by analyzing FedAvg in deep neural networks, finding that diversity in final classification layers hinders performance, and proposes partial variance reduction on these layers, which outperforms benchmarks with similar or lower communication cost.
Data heterogeneity across clients is a key challenge in federated learning. Prior works address this by either aligning client and server models or using control variates to correct client model drift. Although these methods achieve fast convergence in convex or simple non-convex problems, the performance in over-parameterized models such as deep neural networks is lacking. In this paper, we first revisit the widely used FedAvg algorithm in a deep neural network to understand how data heterogeneity influences the gradient updates across the neural network layers. We observe that while the feature extraction layers are learned efficiently by FedAvg, the substantial diversity of the final classification layers across clients impedes the performance. Motivated by this, we propose to correct model drift by variance reduction only on the final layers. We demonstrate that this significantly outperforms existing benchmarks at a similar or lower communication cost. We furthermore provide proof for the convergence rate of our algorithm.