Federated Learning for Non-IID Data via Unified Feature Learning and Optimization Objective Alignment

Abstract

Federated Learning (FL) aims to establish a shared model across decentralized clients under the privacy-preserving constraint. Despite certain success, it is still challenging for FL to deal with non-IID (non-independent and identical distribution) client data, which is a general scenario in real-world FL tasks. It has been demonstrated that the performance of FL will be reduced greatly under the non-IID scenario, since the discrepant data distributions will induce optimization inconsistency and feature divergence issues. Besides, naively minimizing an aggregate loss function in this scenario may have negative impacts on some clients and thus deteriorate their personal model performance. To address these issues, we propose a Unified Feature learning and Optimization objectives alignment method (FedUFO) for non-IID FL. In particular, an adversary module is proposed to reduce the divergence on feature representation among different clients, and two consensus losses are proposed to reduce the inconsistency on optimization objectives from two perspectives. Extensive experiments demonstrate that our FedUFO can outperform the state-of-the-art approaches, including the competitive one data-sharing method. Besides, FedUFO can enable more reasonable and balanced model performance among different clients.