Towards Privacy-Preserving and Verifiable Federated Matrix Factorization

TL;DR

This paper introduces VPFedMF, a novel federated matrix factorization method that ensures privacy and correctness verification of shared gradients, improving security and reliability in federated recommendation systems.

Contribution

It presents a lightweight, secure aggregation scheme with verifiable correctness for federated matrix factorization, addressing privacy and trust issues in existing approaches.

Findings

Achieves privacy-preserving gradient sharing with minimal accuracy loss

Supports correctness verification of aggregation results

Demonstrates practical efficiency on real-world datasets

Abstract

Recent years have witnessed the rapid growth of federated learning (FL), an emerging privacy-aware machine learning paradigm that allows collaborative learning over isolated datasets distributed across multiple participants. The salient feature of FL is that the participants can keep their private datasets local and only share model updates. Very recently, some research efforts have been initiated to explore the applicability of FL for matrix factorization (MF), a prevalent method used in modern recommendation systems and services. It has been shown that sharing the gradient updates in federated MF entails privacy risks on revealing users' personal ratings, posing a demand for protecting the shared gradients. Prior art is limited in that they incur notable accuracy loss, or rely on heavy cryptosystem, with a weak threat model assumed. In this paper, we propose VPFedMF, a new design aimed at privacy-preserving and verifiable federated MF. VPFedMF provides guarantees on the confidentiality of individual gradient updates through lightweight and secure aggregation. Moreover, VPFedMF ambitiously and newly supports correctness verification of the aggregation results produced by the coordinating server in federated MF. Experiments on a real-world movie rating dataset demonstrate the practical performance of VPFedMF in terms of computation, communication, and accuracy.