Abstract:

The model stealing and gradient leakage attacks have increasingly become the bottlenecks that limit the broad application of federated learning.The existing authorization-based intellectual property protection schemes and privacy-preserving federated learning schemes have conducted a lot of research to solve the above challenges.However,there are still issues of authorization invalidation and high computational overhead.To solve the above problems,this paper proposes a model intellectual property and privacy-preserving method in federated learning.This method can protect the privacy of local gradients while ensuring that the aggregated model authorization is not invalidated.Specifically,a lightweight gradient aggregation method based on the blind factor is designed to significantly reduce the computational overhead of the encryption and decryption process by aggregating blinding factors.On this basis,an interactive co-training method based on anti-transfer learning is further proposed to ensure that the model can only be used by authorized users in authorized domains while protecting the privacy of local gradients,where the Shannon mutual information between the representation vector of the auxiliary domain data and the obstacle is increased.The security and correctness of the scheme are theoretically proved,and the system’s superiority is verified on the public data set.It is shown that the performance of the proposed method in the unauthorized domain is at least 47% lower than that of the existing schemes,and the computational complexity is reduced at the level of gradient dimension.

Key words: federated learning, intellectual property protection, non-transfer learning, privacy-preserving, public key cryptography