LightVeriFL: Lightweight and Verifiable Secure Federated Learning

Secure aggregation protocols are implemented in federated learning to protect the local models of the participating users so that the server does not obtain any information beyond the aggregate model at each iteration. However, existing secure aggregation schemes fail to protect the integrity, i.e., correctness, of the aggregate model in the possible presence of a malicious server forging the aggregation result, which motivates the need for verifiable aggregation in federated learning. Existing verifiable aggregation schemes either have a complexity that linearly grows with the model size or require time-consuming reconstruction at the server, that is quadratic in the number of users, in case of likely user dropouts. To overcome these limitations, we propose {\texttt{LightVeriFL}}, a lightweight and communication-efficient secure verifiable aggregation protocol, that provides the same guarantees for verifiability against a malicious server, data privacy, and dropout-resilience as the state-of-the-art protocols without incurring substantial communication and computation overheads. The proposed \texttt{LightVeriFL} protocol utilizes homomorphic hash and commitment functions of constant length, that are independent of the model size, to enable verification at the users. In case of dropouts, \texttt{LightVeriFL} uses a one-shot aggregate hash recovery of the dropped users, instead of a one-by-one recovery based on secret sharing, making the verification process significantly faster than the existing approaches. We evaluate \texttt{LightVeriFL} through experiments and show that it significantly lowers the total verification time in practical settings.