Verifiable and Lightweight Multi-Round Secure Federated Learning

Federated learning (FL) is a paradigm that ensures the confidentiality and accessibility of data without requiring the collection of private data from multiple sources. It acquires an aggregation model by integrating various local models from clients. However, clients are vulnerable to numerous security and privacy threats. Existing solutions were unable to implement training models that are both dropout-resilient and lightweight while also providing verification capabilities when large-scale clients are involved in federated training. To improve the usability of FL, we propose a verifiable and lightweight multi-round secure FL framework by designing and incorporating a double-masking mechanism to ensure secure transmission. Moreover, we optimize the secure aggregation strategy by designing a dropout-resilience method via the secret-sharing mechanism. Specifically, we establish a lightweight model-secure training scheme and provide a parameter reuse strategy by constructing a full connection graph, which reduces computational cost and communication overhead. Furthermore, we propose a secure authentication protocol that enables the client to verify the accuracy of the computing results from the server. Extensive experimental evaluations indicate that our solution demonstrates relatively modest performance but superior functionality compared to current state-of-the-art methods. In particular, we can achieve the verification function with an acceptable increase in computational cost of approximately 200ms per epoch.