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

Author:

Wu Jianping1ORCID,Wu Chunming1,Chen Chaochao1ORCID,Jin Jiahe2,Zhou Chuan34ORCID

Affiliation:

1. College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China

2. Key Laboratory of Key Technologies for Open Data Fusion in Zhejiang Province, Hangzhou 310007, China

3. School of Microelectronics, Tianjin University, Tianjin 300100, China

4. School of Information Engineering, Minzu University of China, Beijing 100081, China

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.

Funder

National Science and Technology Major Project of China

2024 Key R&D Program of Zhejiang Province, China

Publisher

MDPI AG

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