Cooperative Multi-Model Training for Personalized Federated Learning Over Heterogeneous Devices

Federated learning (FL) is an increasingly popular paradigm for protecting data privacy in machine learning systems. However, the data heterogeneity and high computation cost/latency are challenging barriers for employing FL in real-world applications with heterogeneous devices. In this paper, we propose a novel personalized FL framework named CompFL allowing cooperative training of models with varied structures to mitigate those issues. First, CompFL initializes a set of expert models in varied sizes and allows each client to choose one or multiple expert models for training according to their capacities. Second, CompFL combines the model decoupling strategy and local-global feature alignment to mitigate the adverse impact of label heterogeneity, where clients only share the feature extractor part for each model architecture. Third, to encourage mutual enhancement of various models, knowledge distillation in local training is further applied to improve the overall performance. To make our framework workable in real systems, we implement it in both centralized settings with server-coordinated parallel training, and decentralized settings with newly developed device-to-device training-forwarding schemes. Extensive experiments on benchmark datasets are conducted to verify the potential of our framework for personalized FL over heterogeneous devices.