LVSA: Lightweight and verifiable secure aggregation for federated learning

Federated learning (FL) is a decentralized machine learning paradigm that facilitates collaborative training of global models through the exchange of local gradients while maintaining the confidentiality of raw data. However, recent studies have identified gradient leakage attacks and server-forged aggregation results as significant threats to user data privacy. This issue is especially pronounced in large-scale mobile devices (e.g., tablets, smartphones, and smartwatches), which store highly sensitive user data, making the protection of such data critical. In addition, it is essential to consider the limitations of mobile devices, such as potential power outages, disconnections, and their limited computational and communication resources. To address these challenges, LVSA, a lightweight and verifiable secure aggregation scheme is proposed. LVSA employs a non-interactive masking scheme to protect gradient privacy and allows any user to drop out at any stage. Moreover, a lightweight verification method based on the inner product is introduced, which eliminates complex computations and is more suitable for devices with limited computational resources. Security analysis shows that LVSA not only protects users’ original gradients from being leaked, but also verifies the correctness of the aggregation results. Experimental analysis shows that when the gradient dimension reaches ${10}^6$, the computation time in LVSA is two orders of magnitude faster than the most advanced existing schemes. In addition, the communication overhead for users is reduced by more than eight times compared to other schemes offering the same functionality.