Elena Rodríguez-Lois, Fernando Pérez-González · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28076 · open-access PDF
Batch Normalization (BN) layers have become a common choice for embedding white-box watermarks in Federated Learning (FL), providing a simple and effective mechanism for model ownership verification and participant identification. However, previous works have focused on robustness against generic model modifications, leaving the security implications of this design largely unexplored. In this paper, we identify fundamental vulnerabilities of BN-based watermarking schemes and propose a practical data-free attack that exploits them. Experimental results on two representative FL watermarking sche
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