Title: ASAP: Adaptive Sliding Agnostic Poisoning Attack on Federated Learning
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: Agnostic Poisoning Attack; Federated Learning

Abstract: The primary risk in the federated learning (FL) framework arises from the potential for manipulating local training data and updates, known as a poisoning attack. Among various attack strategies, agnostic attacks have emerged as a significant category that attempts to operate without explicit knowledge of the server's aggregation rules (AGRs). However, existing AGR-agnostic attacks still suffer from a critical dependency: they rely heavily on staying inside the natural per-coordinate variance of honest client updates. These attacks typically operate by analyzing benign clients' gradient patterns, statistical properties, and behavioral characteristics to strategically position their malicious updates. Therefore, to overcome these fundamental limitations of current AGR-agnostic attacks, this work presents the Adaptive Sliding Agnostic Poisoning Attack (ASAP) on FL, which can adaptively, robustly and precisely manipulate the degree of poisoning without the knowledge of AGRs algorithm of the server. 

Instead of relying on benign client patterns, ASAP incorporates Adaptive Sliding Model Control (ASMC) theory --- a sophisticated robust nonlinear control framework that enables adaptive attack. We implement our attack through comprehensive experiments on state-of-the-art (SOTA) Byzantine-robust federated learning methods using real-world datasets. These evaluations reveal that ASAP significantly outperforms all existing agnostic attacks while maintaining complete independence from benign client information, representing a fundamental advancement in FL attack strategies.