Personalized Federated Class-Incremental Learning through Critical Parameter Transfer

Abstract

Personalized federated learning is a decentralized approach that enables clients to collaboratively train a shared model while customizing it to their unique data and requirements. However, existing methods are not suited for dynamic scenarios where clients continuously learn from new classes of data, as they struggle with the increased data heterogeneity caused by this influx. To address this, we propose a Personalized Federated Class Incremental Learning (PFCIL) framework based on critical parameter transfer, called CPT-PFCIL, which each local client can continuously learn new classes, and before uploading the local model to the server, the client separates parameters of the local model into global federated parameters and task-specific parameters. Through communication with the server, each client selectively receives knowledge from other clients, achieving continuous learning and updating of the model while providing personalized model optimization. Our method has been validated on three datasets, demonstrating better performance compared to existing personalized federated learning methods.