RobWE: Robust Watermark Embedding for Personalized Federated Learning Model Ownership Protection

arXiv:2402.19054v1 Announce Type: new
Abstract: Embedding watermarks into models has been widely used to protect model ownership in federated learning (FL). However, existing methods are inadequate for protecting the ownership of personalized models acquired by clients in personalized FL (PFL). This is due to the aggregation of the global model in PFL, resulting in conflicts over clients' private watermarks. Moreover, malicious clients may tamper with embedded watermarks to facilitate model leakage and evade accountability. This paper presents a robust watermark …

aggregation arxiv clients cs.ai cs.cr federated federated learning global ownership protect protecting protection watermarks