AggreMark: Efficient Watermarking in Federated Learning via Weighted Averaging

Federated learning has rapidly advanced as a privacy-preserving, distributed machine learning methodology. Protecting the intellectual property rights of federated models, however, poses significant challenges. Existing backdoor-based watermarking techniques suffer from complexity, inefficiency, and implementation challenges. In this paper, we present AggreMark, a novel watermarking approach that incorporates parallel server-side training and watermark embedding during the weighted averaging phase of federated learning. This method does not alter the original model structure or parameters related to the original learning task and adds no additional time overhead, ensuring its practicality for real-world applications. AggreMark is easy to implement and maintain, and it robustly preserves the model’s high performance. We validate the method’s effectiveness and robustness against pruning-based watermark removal attacks through experiments on CIFAR-10.