  Federated learning allows multiple parties to collaborate in learning a
global model without revealing private data. The high cost of training and the
significant value of the global model necessitates the need for ownership
verification of federated learning. However, the existing ownership
verification schemes in federated learning suffer from several limitations,
such as inadequate support for a large number of clients and vulnerability to
ambiguity attacks. To address these limitations, we propose a cryptographic
signature-based federated learning model ownership verification scheme named
FedSOV. FedSOV allows numerous clients to embed their ownership credentials and
verify ownership using unforgeable digital signatures. The scheme provides
theoretical resistance to ambiguity attacks with the unforgeability of the
signature. Experimental results on computer vision and natural language
processing tasks demonstrate that FedSOV is an effective federated model
ownership verification scheme enhanced with provable cryptographic security.
