LES: Lightweight and Efficient Signcryption for Federated Edge Learning in IIoT

Chen Yang, Feilong Lin, Lei Tian, Jiahao Gan, Riheng Jia, Zhonglong Zheng, Minglu Li

Abstract: Federated edge learning (FEL) enables industrial Internet of Things (IIoT) devices to collaboratively train machine learning models without exposing their local data. However, insecure communication environments pose significant threats to the security of model transmission in FEL. Signcryption, as a novel cryptographic primitive, can provide confidentiality, integrity, and other security guarantees for model transmission. Nevertheless, most existing signcryption schemes are designed for a single recipient and therefore cannot meet the multirecipient requirements of FEL. In addition, these schemes lack mechanisms for revoking signcryption privileges, which allows malicious edge nodes to continue participating in model transmission and disrupt the training of the global model. To address these challenges, this article proposes the lightweight and efficient signcryption for FEL in IIoT (LES), which achieves efficient one-to-many signcryption by leveraging bilinear pairings and Lagrange interpolation. LES balances computational and communication overhead while ensuring security. Moreover, the LES scheme incorporates blockchain technology and the Chinese remainder theorem (CRT) to enable revocation of signcryption privileges, preventing compromised edge nodes from continuing to engage in model transmission. Formal security proofs of the LES scheme are provided. A comprehensive comparison between LES and eight representative signcryption schemes proposed in recent years highlights the feasibility of LES and its clear advantages in terms of computational and communication overhead. Under partial participation, LES achieves at least a 29.1% reduction in computational cost and an 81.8% reduction in communication cost compared with the most efficient single recipient scheme among the eight evaluated.

External IDs:dblp:journals/iotj/YangLTGJZL25