物联网感知环境中抗投毒可验证安全联邦学习方案OA北大核心CSTPCD

To address the issue of model poisoning during predictive model training in the IoT intelligent sensing phase,this study proposes an anti-poisoning attack scheme with verification capabilities.The scheme employs a cosine similarity clustering mechanism and a filtering strategy as a trusted third-party detection algorithm,integrating homomorphic encryption for authentication.Additionally,lightweight data encryption is used to protect the privacy of local model data.The Shamir Secret Sharing algorithm ensures robustness in model training against users dropout.By introducing a trusted third party,the scheme effectively detects and prevents dishonest users or attackers from compromising the accuracy of federated learning models.Simulation results demonstrate that the scheme can accurately detect model data involved in training while ensuring the security of users'local model data and handling large volumes of heterogeneous data in IoT intelligent sensing environments.