CLDP-pFedAvg: Safeguarding Client Data Privacy in Personalized Federated Averaging

Wenquan Shen, Shuhui Wu () and Yuanhong Tao
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Wenquan Shen: School of Science, Zhejiang University of Science and Technology, Hangzhou 310023, China
Shuhui Wu: School of Science, Zhejiang University of Science and Technology, Hangzhou 310023, China
Yuanhong Tao: School of Science, Zhejiang University of Science and Technology, Hangzhou 310023, China

Mathematics, 2024, vol. 12, issue 22, 1-23

Abstract: The personalized federated averaging algorithm integrates a federated averaging approach with a model-agnostic meta-learning technique. In real-world heterogeneous scenarios, it is essential to implement additional privacy protection techniques for personalized federated learning. We propose a novel differentially private federated meta-learning scheme, CLDP-pFedAvg, which achieves client-level differential privacy guarantees for federated learning involving large heterogeneous clients. The client-level differentially private meta-based FedAvg algorithm enables clients to upload local model parameters for aggregation securely. Furthermore, we provide a convergence analysis of the clipping-enabled differentially private meta-based FedAvg algorithm. The proposed strategy is evaluated across various datasets, and the findings indicate that our approach offers improved privacy protection while maintaining model accuracy.

Keywords: personalized federated averaging; client-level differential privacy; meta-learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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