LAS: Lightweight Aggregate Signcryption for federated learning with blockchain in IoT

Abstract

Federated learning enables edge servers to share models instead of exchanging data, achieving high-quality model training. Nevertheless, the unreliability of communication environments presents security challenges for model transmission between edge servers. Signcryption can protect the security of the model during transmission, but existing schemes lack efficient pseudonym verification and revocation of signcryption permissions. To address these challenges, this paper proposes Lightweight Aggregate Signcryption for federated learning (LAS). LAS leverages blockchain technology for trusted storage and verification, while incorporating a pseudonym verification mechanism based on relevant proofs instead of repeated verification requests. Furthermore, we introduce a signcryption permission revocation mechanism based on the Chinese Remainder Theorem, ensuring that once a edge server is flagged as malicious, it can no longer generate valid ciphertexts. LAS also supports ciphertext aggregation and batch verification. Finally, we theoretically prove that LAS achieves IND-CCA2 and EUF-CMA security. Extensive experimental results demonstrate its feasibility and advantages in terms of computational overhead, communication overhead, and functionality.