  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.
