Towards Sybil Resilience in Decentralized Learning

TL;DR

This paper introduces SybilWall, a novel algorithm that enhances the resilience of decentralized learning against Sybil poisoning attacks, outperforming existing methods and maintaining accuracy across various attack scenarios.

Contribution

The study presents SybilWall, a new scalable algorithm combining similarity-based aggregation and probabilistic gossiping to improve Sybil attack resistance in decentralized learning.

Findings

SybilWall outperforms existing solutions in resilience.

It maintains consistent accuracy under diverse attack scenarios.

It reduces the effectiveness of attacks with fewer Sybils.

Abstract

Federated learning is a privacy-enforcing machine learning technology but suffers from limited scalability. This limitation mostly originates from the internet connection and memory capacity of the central parameter server, and the complexity of the model aggregation function. Decentralized learning has recently been emerging as a promising alternative to federated learning. This novel technology eliminates the need for a central parameter server by decentralizing the model aggregation across all participating nodes. Numerous studies have been conducted on improving the resilience of federated learning against poisoning and Sybil attacks, whereas the resilience of decentralized learning remains largely unstudied. This research gap serves as the main motivator for this study, in which our objective is to improve the Sybil poisoning resilience of decentralized learning. We present SybilWall, an innovative algorithm focused on increasing the resilience of decentralized learning against targeted Sybil poisoning attacks. By combining a Sybil-resistant aggregation function based on similarity between Sybils with a novel probabilistic gossiping mechanism, we establish a new benchmark for scalable, Sybil-resilient decentralized learning. A comprehensive empirical evaluation demonstrated that SybilWall outperforms existing state-of-the-art solutions designed for federated learning scenarios and is the only algorithm to obtain consistent accuracy over a range of adversarial attack scenarios. We also found SybilWall to diminish the utility of creating many Sybils, as our evaluations demonstrate a higher success rate among adversaries employing fewer Sybils. Finally, we suggest a number of possible improvements to SybilWall and highlight promising future research directions.