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.

本研究旨在提高去中心化学习的 Sybil 义务破坏韧性。我们提出了 SybilWall 算法，该算法基于 Sybils 之间的相似度和新颖的概率传闻机制，确立了一个新的可扩展的，具有 Sybil 韧性的分散式学习基准。全面的实证评估表明，SybilWall 优于针对联邦学习方案设计的现有最先进解决方案，并且是唯一一种在各种敌对攻击情况下获得一致准确度的算法。我们还发现，SybilWall 减少了创建许多 Sybils 的效用，因为我们的评估表明，采用较少 Sybils 的对手的成功率更高。最后，我们提出了 SybilWall 的一些可能改进方法，并强调了有前途的未来研究方向。