Abstract:

During the federated learning, multiple data owners can jointly train a high-quality model, which effectively solves the problem of data silos and protects the privacy of the user data. However, the current federated learning has problems such as model leakage, unverifiable training results, high user computing and communication costs. To solve the above problems, a privacy-enhanced and verifiable security aggregation scheme for federated learning is proposed, which simultaneously realizes the privacy protection of user data and model parameters, and the verifiability of training results. The proposed scheme greatly reduces the computational and communication overhead of users. The scheme uses the homomorphic encryption algorithm to process floating-point operations, and verifies the correctness of the aggregation results based on a linear homomorphic hash function. Even if some users are offline, the final aggregation results will not be affected. The experimental results show that this scheme has less computational overhead and effectively improves the test performance of the trained model.

Key words: federated learning, homomorphic encryption, secure aggregation, verification, privacy enhancement