查看文章

As one of the most important methods of privacy computing, federated learning has attracted much attention as it makes data available but invisible (i.e., uploading gradients instead of raw data). However, adversaries may still recover some private information such as tabs, memberships or even training data, from gradients. Additionally, the malicious server may return the incorrect or forged aggregated result to clients for certain illegal interests. To ensure verifiability and privacy-preservation, in this paper, we present a verifiable secure aggregation scheme under the dual-server federated learning framework. Specifically, we combine the learning with error (LWE) cryptosystem with the secret sharing technique to guarantee the privacy of the aggregated result and each client’s local gradient. Meanwhile, we skillfully design a double-verification protocol, including the server-side and client-side verification, to …