EVFL: Towards Efficient Verifiable Federated Learning via Parameter Reuse and Adaptive Sparsification

Jianping Wu (), Chunming Wu (), Chaochao Chen, Jiahe Jin and Chuan Zhou ()
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Jianping Wu: College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
Chunming Wu: College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
Chaochao Chen: College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
Jiahe Jin: Key Laboratory of Key Technologies for Open Data Fusion in Zhejiang Province, Hangzhou 310007, China
Chuan Zhou: School of Microelectronics, Tianjin University, Tianjin 300100, China

Mathematics, 2024, vol. 12, issue 16, 1-20

Abstract: Federated learning (FL) demonstrates significant potential in Industrial Internet of Things (IIoT) settings, as it allows multiple institutions to jointly construct a shared learning model by exchanging model parameters or gradient updates without the need to transmit raw data. However, FL faces risks related to data poisoning and model poisoning. To address these issues, we propose an efficient verifiable federated learning (EVFL) method, which integrates adaptive gradient sparsification (AdaGS), Boneh–Lynn–Shacham (BLS) signatures, and fully homomorphic encryption (FHE). The combination of BLS signatures and the AdaGS algorithm is used to build a secure aggregation protocol. These protocols verify the integrity of parameters uploaded by industrial agents and the consistency of the server’s aggregation results. Simulation experiments demonstrate that the AdaGS algorithm significantly reduces verification overhead through parameter sparsification and reuse. Our proposed algorithm achieves better verification efficiency compared to existing solutions.

Keywords: federated learning; data poisoning; situation prediction; privacy protection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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