Blockchain and signcryption enabled asynchronous federated learning framework in fog computing
Federated learning combines with fog computing to transform data sharing into model sharing, which solves the issues of data isolation and privacy disclosure in fog computing. However, existing studies focus on centralized single-layer aggregation federated learning architecture, which lack the cons...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-04-01
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| Series: | Digital Communications and Networks |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352864824000336 |
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| author | Zhou Zhou Youliang Tian Jinbo Xiong Changgen Peng Jing Li Nan Yang |
| author_facet | Zhou Zhou Youliang Tian Jinbo Xiong Changgen Peng Jing Li Nan Yang |
| author_sort | Zhou Zhou |
| collection | DOAJ |
| description | Federated learning combines with fog computing to transform data sharing into model sharing, which solves the issues of data isolation and privacy disclosure in fog computing. However, existing studies focus on centralized single-layer aggregation federated learning architecture, which lack the consideration of cross-domain and asynchronous robustness of federated learning, and rarely integrate verification mechanisms from the perspective of incentives. To address the above challenges, we propose a Blockchain and Signcryption enabled Asynchronous Federated Learning (BSAFL) framework based on dual aggregation for cross-domain scenarios. In particular, we first design two types of signcryption schemes to secure the interaction and access control of collaborative learning between domains. Second, we construct a differential privacy approach that adaptively adjusts privacy budgets to ensure data privacy and local models' availability of intra-domain user. Furthermore, we propose an asynchronous aggregation solution that incorporates consensus verification and elastic participation using blockchain. Finally, security analysis demonstrates the security and privacy effectiveness of BSAFL, and the evaluation on real datasets further validates the high model accuracy and performance of BSAFL. |
| format | Article |
| id | doaj-art-2c73a8451e74491fb55d937035a08c77 |
| institution | OA Journals |
| issn | 2352-8648 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Digital Communications and Networks |
| spelling | doaj-art-2c73a8451e74491fb55d937035a08c772025-08-20T01:49:01ZengKeAi Communications Co., Ltd.Digital Communications and Networks2352-86482025-04-0111244245410.1016/j.dcan.2024.03.004Blockchain and signcryption enabled asynchronous federated learning framework in fog computingZhou Zhou0Youliang Tian1Jinbo Xiong2Changgen Peng3Jing Li4Nan Yang5State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, ChinaState Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China; Corresponding author at: State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China.Fujian Provincial Key Laboratory of Network Security and Cryptology, College of Computer and Cyber Security, Fujian Normal University, Fuzhou 350117, China; Guangxi Key Laboratory of Cryptography and Information Security, Guilin University of Electronic Technology, Guilin 541004, China; Corresponding author at: Fujian Provincial Key Laboratory of Network Security and Cryptology, College of Computer and Cyber Security, Fujian Normal University, Fuzhou 350117, China.State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, ChinaState Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, ChinaState Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, ChinaFederated learning combines with fog computing to transform data sharing into model sharing, which solves the issues of data isolation and privacy disclosure in fog computing. However, existing studies focus on centralized single-layer aggregation federated learning architecture, which lack the consideration of cross-domain and asynchronous robustness of federated learning, and rarely integrate verification mechanisms from the perspective of incentives. To address the above challenges, we propose a Blockchain and Signcryption enabled Asynchronous Federated Learning (BSAFL) framework based on dual aggregation for cross-domain scenarios. In particular, we first design two types of signcryption schemes to secure the interaction and access control of collaborative learning between domains. Second, we construct a differential privacy approach that adaptively adjusts privacy budgets to ensure data privacy and local models' availability of intra-domain user. Furthermore, we propose an asynchronous aggregation solution that incorporates consensus verification and elastic participation using blockchain. Finally, security analysis demonstrates the security and privacy effectiveness of BSAFL, and the evaluation on real datasets further validates the high model accuracy and performance of BSAFL.http://www.sciencedirect.com/science/article/pii/S2352864824000336BlockchainSigncryptionFederated learningAsynchronousFog computing |
| spellingShingle | Zhou Zhou Youliang Tian Jinbo Xiong Changgen Peng Jing Li Nan Yang Blockchain and signcryption enabled asynchronous federated learning framework in fog computing Digital Communications and Networks Blockchain Signcryption Federated learning Asynchronous Fog computing |
| title | Blockchain and signcryption enabled asynchronous federated learning framework in fog computing |
| title_full | Blockchain and signcryption enabled asynchronous federated learning framework in fog computing |
| title_fullStr | Blockchain and signcryption enabled asynchronous federated learning framework in fog computing |
| title_full_unstemmed | Blockchain and signcryption enabled asynchronous federated learning framework in fog computing |
| title_short | Blockchain and signcryption enabled asynchronous federated learning framework in fog computing |
| title_sort | blockchain and signcryption enabled asynchronous federated learning framework in fog computing |
| topic | Blockchain Signcryption Federated learning Asynchronous Fog computing |
| url | http://www.sciencedirect.com/science/article/pii/S2352864824000336 |
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