arXiv:2602.10765v2 Announce Type: replace Abstract: In federated learning (FL), $K$ clients jointly train a model without sharing raw data. Because each participant invests data and compute, clients need mechanisms to later prove the provenance of a jointly trained model. Model watermarking embeds a hidden signal in the weights, but naive approaches either do not scale with many clients as per-client watermarks dilute as $K$ grows, or give any individual client the ability to verify and potentially remove the watermark. We introduce $(t,K)$-threshold watermarking: clients collaboratively embed
The proliferation of federated learning models and the increasing need for intellectual property protection and provenance in collaborative AI development drives this innovation.
This research provides a robust mechanism for proving model provenance and intellectual property in a decentralized AI development context, addressing trust and ownership issues critical for widespread adoption.
The ability to securely watermark collaboratively trained AI models without central authority ensures client contribution recognition and deters unauthorized use, altering the dynamics of AI co-development.
- · Federated Learning platforms
- · AI model developers
- · Data contributors
- · AI IP protection services
- · Malicious actors diluting watermarks
- · Unsophisticated watermark techniques
- · Unethical model re-users
Increased trust and collaboration in federated learning environments due to assured provenance.
New business models emerge around verifiable AI intellectual property and federated data contributions.
The development of a global standard for AI model provenance, accelerating cross-border AI development and adoption.
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Read at arXiv cs.LG