<scp>TreePPFL</scp> : A Verifiable Secure and Efficient Federated Learning Framework

ABSTRACT In the context of federated learning, there are certain doubts about the credibility of cloud servers as third parties, as they have the potential to extract sensitive information from participants' local data from gradients. In addition, cloud servers may even resort to forging aggregation results, leading to the destruction of the global model and successfully avoiding detection mechanisms. Therefore, in building a secure federated learning system, it is crucial to ensure the privacy and aggregation correctness of upload gradients. This article proposes a secure and efficient federated learning privacy protection scheme TreePPFL (Tree Privacy Protection Federated Learning) based on hop‐by‐hop communication verifiability. By using single concealment technology to encrypt model parameters, the privacy of the uploaded gradient is protected. At the same time, a bidirectional verification scheme was designed, which applies a homomorphic hash algorithm to enable the cloud server to verify the legitimacy of the client, while also enabling the client to verify the aggregation correctness of the cloud server. The client transmits information through hop‐by‐hop communication, improving the training and verification efficiency of the cloud server and the entire federated learning system. The security of the scheme was verified through security analysis. The empirical experiment used two publicly available datasets, MNIST and CIFAR‐100, and compared them in iid and noniid scenarios. The results showed that the TreePPFL scheme exhibited superior performance compared to other schemes.