Enhancing Copyright Protection in Federated Learning: A Hierarchical Watermarking Approach

Abstract

Copyright protection in Federated Learning (FL) environments faces some challenges, such as fidelity, validity, robustness, and persistence, due to the large number of diverse FL clients. To address these issues, we propose a Grouped Clients Hierarchical Watermarking (GCHW) scheme, which consists of client grouping, watermark generation, watermark embedding and watermark verification. The client grouping is to group clients based on shared characteristics. In watermark generation, a hierarchical framework is proposed to establish dependencies between the group and client watermarks by embedding part of the group watermark information into each client watermark. Moreover, our GCHW scheme innovatively embeds binary watermarks into the frequency domain of model parameters at both the group and client levels during the watermark embedding phase. Experimental results demonstrate that GCHW effectively maintains fidelity and the validity of the watermarks is validated across independent and identically distributed (IID) and non-IID scenarios. GCHW also exhibits strong robustness against common attacks and defensive aggregation algorithms. In addition, GCHW shows good persistence in dynamic FL environments.