EPFL: Toward Elastic Personalized Federated Learning With Seamless Client Joining and Quitting

Personalized federated learning (PFL) has emerged as an efficient way to tailor global models to individual data features. However, in real-world deployments, the dynamic nature of client participation—where clients may join or quit arbitrarily—poses significant challenges for PFL systems. We propose elastic PFL (EPFL), an EPFL framework that accommodates dynamic client participation while preserving model performance. Our framework addresses two fundamental challenges: 1) seamlessly generating personalized models for newly joining unlabeled clients and 2) achieving efficient and precise multigrain unlearning for quitting clients. To address these challenges, we utilize hypernetworks for client-specific model generation and introduce a novel client embedding regularization (CER) to enhance generalization capabilities for new clients. Furthermore, we design an adversarial purification mechanism that enables efficient multigrained unlearning at the client, sample, and class levels. We provide theoretical analysis establishing both generalization bounds for new clients and convergence guarantees for the unlearning process. Extensive experimental results demonstrate that EPFL outperforms the existing PFL methods in terms of both accuracy and generalization capabilities. Through backdoor trigger experiments, we show that EPFL achieves unlearning efficacy and fidelity comparable to complete retraining while requiring only 3%–10% of the computational time of these existing approaches.