Personalized Graph Federated Learning with Differential Privacy

TL;DR

This paper introduces a personalized graph federated learning framework that preserves privacy using differential privacy techniques, enabling collaborative, device-specific models even with diverse data distributions.

Contribution

It proposes a novel PGFL framework with differential privacy, providing convergence guarantees and bounded deviations, enhancing personalized federated learning with privacy preservation.

Findings

Converges to optimal cluster-specific solutions in linear time.

Ensures local differential privacy for all clients.

Demonstrates effectiveness on synthetic and MNIST datasets.

Abstract

This paper presents a personalized graph federated learning (PGFL) framework in which distributedly connected servers and their respective edge devices collaboratively learn device or cluster-specific models while maintaining the privacy of every individual device. The proposed approach exploits similarities among different models to provide a more relevant experience for each device, even in situations with diverse data distributions and disproportionate datasets. Furthermore, to ensure a secure and efficient approach to collaborative personalized learning, we study a variant of the PGFL implementation that utilizes differential privacy, specifically zero-concentrated differential privacy, where a noise sequence perturbs model exchanges. Our mathematical analysis shows that the proposed privacy-preserving PGFL algorithm converges to the optimal cluster-specific solution for each cluster in linear time. It also shows that exploiting similarities among clusters leads to an alternative output whose distance to the original solution is bounded, and that this bound can be adjusted by modifying the algorithm's hyperparameters. Further, our analysis shows that the algorithm ensures local differential privacy for all clients in terms of zero-concentrated differential privacy. Finally, the performance of the proposed PGFL algorithm is examined by performing numerical experiments in the context of regression and classification using synthetic data and the MNIST dataset.