Privacy-Aligned Personalized Federated Learning with Compact Adaptation and Variable-Length Gaussian Communication

2026-09-14

AI Summary

本文解决了个性化联邦学习中的隐私对齐问题，通过一次性发布私有客户端上下文和在固定系数空间中进行重复适应，并采用可变长度高斯通信减少通信成本。

Abstract

Record-level differential privacy exposes a structural misalignment in personalized federated learning when client-specific variation is low-dimensional while training repeatedly releases high-dimensional updates. In this paper, we address this misalignment by releasing a private client context once and confining repeated adaptation to a fixed coefficient space. Beyond dimensionality reduction, the factorized generator induces an adaptive optimization geometry that reshapes noisy updates, and controlled ablations show that most of its private-training gain is retained by radial evolution. To further reduce the communication cost, we realize the Gaussian mechanism for coefficient updates directly through variable-length quantization with finite expected code length, so that the quantization error itself serves as the required privacy perturbation rather than extra distortion. Across MNIST and CIFAR-10, our design matches or outperforms full-model private adaptation across privacy budgets and client heterogeneity, while reducing protected uplink by a factor of 2.67 at \(\varepsilon=16\) on CIFAR-10 with comparable future-client accuracy.

Problem

• differential privacy
• personalized federated learning
• client-specific variation

Innovation

• Differential Privacy
• Personalized Federated Learning
• Compact Adaptation
• Gaussian Mechanism
• Variable-Length Quantization