Federated learning provides clients with a means of collaboratively training a global model without sharing their local data, managed by a central server. However, this server cannot always be trusted, as it may act dishonestly and compromise the privacy of clients’ local data. Consequently, mechanisms for privacy preservation and aggregation verification become crucial components of a secure federated learning system. In addition, support for weighted aggregation is also essential to address the challenges posed by non-IID training data. In this article, we present the Privacy-Preserving and Verifiable Weighted Aggregation (PVWA) scheme. Our approach introduces a new privacy-preserving solution by leveraging masking and homomorphic encryption techniques to protect local and global models, respectively. The masking protocol facilitates secure weighted aggregation, whereas a verification mechanism based upon homomorphic hashing and bilinear aggregated signatures ensures the correctness of aggregated results. Experimental evaluations of the performance, compared against alternative methods on two datasets, demonstrate its effectiveness and efficiency.

Multi-signature scheme is a unique type of digital signature where a group of participants are capable of producing a signature interactively on a shared message, thus significantly reducing the signature size. This is especially important for Internet of Vehicles (IoV) systems where higher efficiency and lower costs are required during the communication. Most approaches so far, however, are developed by traditional methods such as the integer factoring assumption, which result in potential vulnerability to quantum computing attacks. Although a few lattice-based multi-signature candidates have been proposed, they either rely on hash-and-sign process with higher costs or may be compromised by larger size of public key and signature. Motivated by the Bimodal Lattice Signature Scheme (BLISS) model [1], we propose a new lattice-based multi-signature scheme (Multi-BLISS, MB) in this paper. Our scheme can also be transformed into an aggregate signature scheme (Aggregate MB, AMB) with similar level of performance. We evaluate both schemes by setting security levels of 128, 160 and 192 bits in the experiments, and the results demonstrate significant improvement on security and efficiency comparing to existing lattice-based multi-signature schemes.