Personalized Hierarchical Federated Learning Algorithm Based on Adaptive Differential Privacy
DOI:
https://doi.org/10.54097/2f97h082Keywords:
Adaptive Differential Privacy, Personalized Federated Learning, Hierarchical Federated LearningAbstract
This paper proposes ADP-PHFL, an Adaptive Differential Privacy-based Personalized Hierarchical Federated Learning framework. It integrates hierarchical federated learning with personalized training via knowledge transfer, mitigating performance degradation caused by directly applying a global model while reducing communication overhead. An adaptive differential privacy mechanism is introduced to allocate privacy budgets more effectively, avoiding the drawbacks of uniform noise injection and enhancing privacy protection against data leakage. Theoretical analysis establishes the convergence and privacy guarantees of the proposed method. Experimental results demonstrate that ADP-PHFL achieves improved communication efficiency and higher model accuracy.
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