Towards Verifiable Federated Learning

TL;DR

This paper reviews the emerging field of verifiable federated learning, highlighting its importance in ensuring trust and reliability in collaborative machine learning while addressing verification challenges.

Contribution

It introduces a novel taxonomy for verifiable FL, summarizes evaluation methods, and discusses future directions for developing versatile verification frameworks.

Findings

Provides a comprehensive survey of verifiable FL techniques

Proposes a taxonomy covering centralized and decentralized FL

Identifies promising research directions for verification methods

Abstract

Federated learning (FL) is an emerging paradigm of collaborative machine learning that preserves user privacy while building powerful models. Nevertheless, due to the nature of open participation by self-interested entities, it needs to guard against potential misbehaviours by legitimate FL participants. FL verification techniques are promising solutions for this problem. They have been shown to effectively enhance the reliability of FL networks and help build trust among participants. Verifiable federated learning has become an emerging topic of research that has attracted significant interest from the academia and the industry alike. Currently, there is no comprehensive survey on the field of verifiable federated learning, which is interdisciplinary in nature and can be challenging for researchers to enter into. In this paper, we bridge this gap by reviewing works focusing on verifiable FL. We propose a novel taxonomy for verifiable FL covering both centralised and decentralised FL settings, summarise the commonly adopted performance evaluation approaches, and discuss promising directions towards a versatile verifiable FL framework.