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The Impact of Adversarial Node Placement in Decentralized Federated Learning Networks by Piaseczny, Adam; Ruzomberka, Eric; Parasnis, Rohit; Brinton, Christopher G. is a scholarly article available to read on EtoBox.

What is The Impact of Adversarial Node Placement in Decentralized Federated Learning Networks about?

As Federated Learning (FL) grows in popularity, new decentralized frameworks are becoming widespread. These frameworks leverage the benefits of decentralized environments to enable fast and energy-efficient inter-device communication. However, this growing popularity also intensifies the need for robust security measures. While existing research has explored various aspects of FL security, the role of adversarial node placement in decentralized networks remains largely unexplored. This paper addresses this gap by analyzing the performance of decentralized FL for various adversarial placement strategies when adversaries can jointly coordinate their placement within a network. We establish two baseline strategies for placing adversarial node: random placement and network centrality-based placement. Building on this foundation, we propose a novel attack algorithm that prioritizes adversarial spread over adversarial centrality by maximizing the average network distance between adversaries. We show that the new attack algorithm significantly impacts key performance metrics such as testing accuracy, outperforming the baseline frameworks by between $9\%$ and $66.5\%$ for the considered se

Author

Piaseczny, Adam; Ruzomberka, Eric; Parasnis, Rohit; Brinton, Christopher G.

Published

2023

Language

EN