Diurnal or Nocturnal? Federated Learning of Multi-Branch Networks from Periodically Shifting Distributions

Abstract

Federated learning has been deployed to train machine learning models from decentralized client data on mobile devices in practice. The clients available for training are observed to have periodically shifting distributions changing with the time of day, which can cause instability in training and degrade the model performance. In this paper, instead of modeling the distribution shift with a block-cyclic pattern as previous works, we model it with a mixture of distributions that gradually shifts between daytime and nighttime modes, and find this intuitive model to better match the observations in practical federated learning systems. Furthermore, we propose to jointly train a clustering model and a multi-branch network to allocate lightweight specialized branches to clients from different modes. A temporal prior is used to significantly boost the training performance. Experiments for image classification on EMNIST and CIFAR datasets, and next word prediction on the Stack Overflow dataset show that the proposed algorithm can counter the effects of the distribution shift and significantly improve the final model performance.