FedThief: Harming Others to Benefit Oneself in Self-Centered Federated Learning

--In federated learning, participants' uploaded model updates cannot be directly verified, leaving the system vulnerable to malicious attacks. Existing attack strategies have adversaries upload tampered model updates to degrade the global model's performance. In real-world scenarios, attackers are driven by self-centered motives: their goal is to gain a competitive advantage by developing a model that outperforms those of other participants, not merely to cause disruption. In this paper, we study a novel Self-Centered Federated Learning (SCFL) attack paradigm, in which attackers not only degrade the performance of the global model through attacks but also enhance their own models within the federated learning process. We propose a framework named FedThief, which degrades the performance of the global model by uploading modified content during the upload stage. At the same time, it enhances the private model's performance through divergence-aware ensemble techniques--where "divergence" quantifies the deviation between private and global models--that integrate global updates and local knowledge. Extensive experiments show that our method effectively degrades the global model performance while allowing the attacker to obtain an ensemble model that significantly outperforms the global model. N the field of machine learning, the quality and diversity of the training data are widely recognized as essential prerequisites for enabling models to generalize effectively to unseen data and perform reliably across a range of downstream tasks [1], [2]. These characteristics directly influence the learned model's empirical risk minimization, hypothesis space coverage, and robustness to distributional shifts [3], [4].