Personalized Federated Learning With Structure

Knowledge sharing and model personalization are two key components to impact the performance of personalized federated learning (PFL). Existing PFL methods simply treat knowledge sharing as an aggregation of all clients regardless of the hidden relations among them. This paper is to enhance the knowledge-sharing process in PFL by leveraging the structural information among clients. We propose a novel structured federated learning(SFL) framework to simultaneously learn the global model and personalized model using each client's local relations with others and its private dataset. This proposed framework has been formulated to a new optimization problem to model the complex relationship among personalized models and structural topology information into a unified framework. Moreover, in contrast to a pre-defined structure, our framework could be further enhanced by adding a structure learning component to automatically learn the structure using the similarities between clients' models' parameters. By conducting extensive experiments, we first demonstrate how federated learning can be benefited by introducing structural information into the server aggregation process with a real-world dataset, and then the effectiveness of the proposed method has been demonstrated in varying degrees of data non-iid settings.