RobWE: Robust Watermark Embedding for Personalized Federated Learning Model Ownership Protection introduces an innovative technique to safeguard the ownership of personalized federated learning models against tampering and misuse. RobWE focuses on bypassing conflicts arising from model aggregation by decoupling watermark embedding into private and shared components. A dedicated watermark slice embedding operation and malicious watermark detection scheme are key elements of the robust methodology devised to enhance the fidelity, reliability, and robustness of the ownership protection.

Key Findings:

• RobWE’s decoupling strategy allows private watermarking of personalized models without interference during global aggregation.
• The watermark slice embedding mitigates conflicts and the malicious detection scheme enhances security.
• Comprehensive testing showcases RobWE’s superior performance compared to current state-of-the-art watermarking methods in federated learning.

This breakthrough presents an essential contribution to the realm of model security, providing rights owners with an effective tool to assert and maintain their ownership over distributed learning environments. It reinforces the need for robust protection measures as personalized federated learning continues to gain traction in various sectors.

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