Communication-Efficient and Model-Heterogeneous Personalized Federated Learning via Clustered Knowledge Transfer

Personalized federated learning (PFL) aims to train model(s) that can perform well on the individual edge-devices' data where the edge-devices (clients) are usually IoT devices like our mobile phones. The participating clients for cross-device settings, in general, have heterogeneous system capabilities and limited communication bandwidth. Such practical properties of the edge-devices, however, are overlooked by many recent work in PFL, which use the same model architecture across all clients and incur high communication cost by directly communicating the model parameters. In our work, we propose a novel and practical PFL framework named COMET where clients can use heterogeneous models of their own choice and do not directly communicate their model parameters to other parties. Instead, COMET uses clustered codistillation , where clients use knowledge distillation to transfer their knowledge to other clients with similar data distributions. This presents a practical PFL framework for the edge-devices to train through IoT networks by lifting the heavy communication burden of communicating large models. We theoretically show the convergence and generalization properties of COMET and empirically show that COMET achieves high test accuracy with several orders of magnitude lower communication cost while allowing client model heterogeneity compared to the other state-of-the-art PFL methods.