Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling
Journal:
arXiv
Published Date:
Jun 3, 2025
Abstract
Personalized federated learning (PFL) offers a solution to balancing
personalization and generalization by conducting federated learning (FL) to
guide personalized learning (PL). Little attention has been given to wireless
PFL (WPFL), where privacy concerns arise. Performance fairness of PL models is
another challenge resulting from communication bottlenecks in WPFL. This paper
exploits quantization errors to enhance the privacy of WPFL and proposes a
novel quantization-assisted Gaussian differential privacy (DP) mechanism. We
analyze the convergence upper bounds of individual PL models by considering the
impact of the mechanism (i.e., quantization errors and Gaussian DP noises) and
imperfect communication channels on the FL of WPFL. By minimizing the maximum
of the bounds, we design an optimal transmission scheduling strategy that
yields min-max fairness for WPFL with OFDMA interfaces. This is achieved by
revealing the nested structure of this problem to decouple it into subproblems
solved sequentially for the client selection, channel allocation, and power
control, and for the learning rates and PL-FL weighting coefficients.
Experiments validate our analysis and demonstrate that our approach
substantially outperforms alternative scheduling strategies by 87.08%, 16.21%,
and 38.37% in accuracy, the maximum test loss of participating clients, and
fairness (Jain's index), respectively.
