GHEFL: Grouping Based on Homomorphic Encryption Validates Federated Learning
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,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/17/3/128 |
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| author | Yulin Kang Wuzheng Tan Linlin Fan Yinuo Chen Xinbin Lai Jian Weng |
| author_facet | Yulin Kang Wuzheng Tan Linlin Fan Yinuo Chen Xinbin Lai Jian Weng |
| author_sort | Yulin Kang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3981948ec8be4f7e94fcc417191f26c1 |
| institution | DOAJ |
| issn | 1999-5903 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj-art-3981948ec8be4f7e94fcc417191f26c12025-08-20T02:42:32ZengMDPI AGFuture Internet1999-59032025-03-0117312810.3390/fi17030128GHEFL: Grouping Based on Homomorphic Encryption Validates Federated LearningYulin Kang0Wuzheng Tan1Linlin Fan2Yinuo Chen3Xinbin Lai4Jian Weng5College of Cyber Security, Jinan University, Guangzhou 511436, ChinaCollege of Cyber Security, Jinan University, Guangzhou 511436, ChinaCollege of Cyber Security, Jinan University, Guangzhou 511436, ChinaCollege of Cyber Security, Jinan University, Guangzhou 511436, ChinaCollege of Cyber Security, Jinan University, Guangzhou 511436, ChinaCollege of Cyber Security, Jinan University, Guangzhou 511436, ChinaFederated 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.https://www.mdpi.com/1999-5903/17/3/128data privacyfederated learningverifiabilitymulti-servershomomorphic encryption |
| spellingShingle | Yulin Kang Wuzheng Tan Linlin Fan Yinuo Chen Xinbin Lai Jian Weng GHEFL: Grouping Based on Homomorphic Encryption Validates Federated Learning Future Internet data privacy federated learning verifiability multi-servers homomorphic encryption |
| title | GHEFL: Grouping Based on Homomorphic Encryption Validates Federated Learning |
| title_full | GHEFL: Grouping Based on Homomorphic Encryption Validates Federated Learning |
| title_fullStr | GHEFL: Grouping Based on Homomorphic Encryption Validates Federated Learning |
| title_full_unstemmed | GHEFL: Grouping Based on Homomorphic Encryption Validates Federated Learning |
| title_short | GHEFL: Grouping Based on Homomorphic Encryption Validates Federated Learning |
| title_sort | ghefl grouping based on homomorphic encryption validates federated learning |
| topic | data privacy federated learning verifiability multi-servers homomorphic encryption |
| url | https://www.mdpi.com/1999-5903/17/3/128 |
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