  Federated learning is an emerging privacy-preserving distributed machine
learning that enables multiple parties to collaboratively learn a shared model
while keeping each party's data private. However, federated learning faces two
main problems: semi-honest server privacy inference attacks and malicious
client-side model theft. To address privacy inference attacks, parameter-based
encrypted federated learning secure aggregation can be used. To address model
theft, a watermark-based intellectual property protection scheme can verify
model ownership. Although watermark-based intellectual property protection
schemes can help verify model ownership, they are not sufficient to address the
issue of continuous model theft by uncaught malicious clients in federated
learning. Existing IP protection schemes that have the ability to track
traitors are also not compatible with federated learning security aggregation.
Thus, in this paper, we propose a Federated Client-side Intellectual Property
Protection (FedCIP), which is compatible with federated learning security
aggregation and has the ability to track traitors. To the best of our
knowledge, this is the first IP protection scheme in federated learning that is
compatible with secure aggregation and tracking capabilities.
