SAEV-FL: Lightweight Secure Aggregation and Efficient Verification Scheme for Federated Learning in Cloud-Edge Collaborative Environment

In a cloud-edge collaborative environment, especially for the Internet of Vehicles, federated learning (FL) has garnered widespread attention due to its unique training process, where users solely upload trained parameters yet do not transmit their local data, demonstrating that FL is a promising privacy-preserving distributed machine learning paradigm. However, FL still faces challenges, such as the local gradients and global parameters (i.e., global model, global weights, or global gradients) transmitted by users may leak the users' private information, and malicious or lazy aggregation servers may forge or tamper with the parameters uploaded by users, thereby generating incorrect aggregated results, ultimately reducing the availability of the global model. Moreover, network fluctuations or device failures may cause user dropouts during training. Although existing works address these challenges, privacy protection schemes based on complex cryptographic primitives are costly and lack research on protecting global parameters. Additionally, verification schemes for aggregation results face overhead and security challenges. For such, we propose a lightweight secure aggregation and efficient verification scheme for federated learning, namely SAEV-FL. We design a single-masking protocol based on the Chinese Remainder Theorem (CRT) and perturbation technique to achieve privacy protection for local gradients and global parameters with lower overhead. To verify the correctness of the aggregated results, we have combined homomorphic hash functions and random number technology to design a secure verification mechanism that does not disclose users' privacy. Detailed theoretical analysis and comprehensive experiments establish that the proposed scheme outperforms other similar works in terms of security and efficiency.