Onboarding Without Forgetting: Hypernetwork Personalization with Data-Free Replay for Personalized Federated Learning

Abstract:Federated Learning (FL) enables collaborative training across distributed clients without sharing raw data, offering strong privacy benefits. However, most methods assume all clients remain available throughout training, which is unrealistic as new clients often join over time. We study this setting, where the task and label space stay fixed but clients arrive in batches. Our analysis reveals two key challenges: updating the shared model only with new clients harms existing clients, while freezing it protects them but blocks gains from new knowledge. To capture these trade-offs, we introduce Proactive Adaptation (PA) for onboarding gains and Retroactive Improvement (RI) for changes in earlier clients without retraining. We then propose pFedDSH, which combines a central hypernetwork for personalized initialization, batch-specific binary masks for capacity preservation and allocation, and server-side data-free replay to propagate improvements without exposing client data. Experiments show that pFedDSH preserves stability for existing clients while keeping communication and adaptation costs unchanged for new clients.

Machine Learning (cs.LG)

arXiv:2508.05157 [cs.LG]

(or arXiv:2508.05157v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2508.05157 Focus to learn more arXiv-issued DOI via DataCite

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