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The paper investigates how adversarial examples transfer between client models in federated learning and explores the relationship between these examples and client data distributions. It proposes a defense strategy based on adversarial training that leverages the transferability of model robustness. Experiments on real-life datasets demonstrate that the new attack and defense methods outperform existing state‑of‑the‑art approaches.
arXiv:2608. 01095v1 Announce Type: new Abstract: Federated learning (FL) enables multiple intelligent devices to collaboratively train a high-accuracy model without sharing raw data.
The development of federated learning (FL) techniques has helped improve the privacy preservation of users' data and extended the applications of machine learning models. However, the involvement of a...
arXiv:2608. 12962v1 Announce Type: new Abstract: Vertical Federated Learning (VFL) enables organizations holding complementary features of shared entities to collaborate and train models.
The paper investigates how the strategic placement of Byzantine nodes in decentralized federated learning (DFL) affects the propagation of malicious influence across the communication graph. It introduces Byzantine Placement Influence (BPI), a measure that captures cumulative exposure of honest nodes to Byzantine sources over time, and develops algorithms to optimize BPI across various network structures and attack types. Experiments demonstrate that BPI-guided placements consistently yield highly damaging configurations, highlighting the importance of considering node placement in DFL threat models.
arXiv:2608. 06637v1 Announce Type: cross Abstract: Robust aggregation methods are widely used in federated learning to mitigate the impact of adversarial client behavior.