GHEFL: Grouping Based on Homomorphic Encryption Validates Federated Learning

Yulin Kang, Wuzheng Tan (), Linlin Fan, Yinuo Chen, Xinbin Lai and Jian Weng
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Yulin Kang: College of Cyber Security, Jinan University, Guangzhou 511436, China
Wuzheng Tan: College of Cyber Security, Jinan University, Guangzhou 511436, China
Linlin Fan: College of Cyber Security, Jinan University, Guangzhou 511436, China
Yinuo Chen: College of Cyber Security, Jinan University, Guangzhou 511436, China
Xinbin Lai: College of Cyber Security, Jinan University, Guangzhou 511436, China
Jian Weng: College of Cyber Security, Jinan University, Guangzhou 511436, China

Future Internet, 2025, vol. 17, issue 3, 1-16

Abstract: Federated learning is a powerful tool for securing participants’ private data due to its ability to make data “available but not visible”. In recent years, federated learning has been enhanced by the emergence of multi-weight aggregation protocols, which minimize the impact of erroneous parameters, and verifiable protocols, which prevent server misbehavior. However, it still faces significant security and performance challenges. Malicious participants may infer the private data of others or carry out poisoning attacks to compromise the model’s correctness. Similarly, malicious servers may return incorrect aggregation results, undermining the model’s convergence. Furthermore, substantial communication overhead caused by interactions between participants or between participants and servers hinders the development of federated learning. In response to this, this paper proposes GHEFL, a group-based, verifiable, federated learning method based on homomorphic encryption that aims to prevent servers from maliciously stealing participant privacy data or performing malicious aggregation. While ensuring the usability of the aggregated model, it strives to minimize the workload on the server as much as possible. Finally, we experimentally evaluate the performance of GHEFL.

Keywords: data privacy; federated learning; verifiability; multi-servers; homomorphic encryption (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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