Verifiable secure aggregation scheme for privacy protection in federated learning networks
Abstract Federated learning enables multiple participants to construct a distributed machine learning system coordinated by a server. Most existing solutions assume a semi-honest system, considering each participant to be honest but curious, which does not align with the complex real-world environme...
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| Format: | Article |
| Language: | English |
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Springer
2025-08-01
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| Series: | Discover Computing |
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| Online Access: | https://doi.org/10.1007/s10791-025-09676-1 |
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| _version_ | 1849225967961112576 |
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| author | Wujun Yao Tanping Zhou Yiliang Han Xiaolin Wang |
| author_facet | Wujun Yao Tanping Zhou Yiliang Han Xiaolin Wang |
| author_sort | Wujun Yao |
| collection | DOAJ |
| description | Abstract Federated learning enables multiple participants to construct a distributed machine learning system coordinated by a server. Most existing solutions assume a semi-honest system, considering each participant to be honest but curious, which does not align with the complex real-world environment. In reality, servers might act maliciously by tampering with or forging aggregation results, which directly threatens the integrity of global models.. To verify the integrity of server aggregation computations while protecting the privacy of clients, this paper introduces a privacy-preserving verifiable secure aggregation scheme for federated learning networks. Initially, we construct a functional reuse private key ring generation algorithm, enabling clients to encrypt and protect their private gradients using the private key ring. Subsequently, leveraging the discrete logarithm difficulty problem, we devise a commitment protocol where clients commit to their encrypted private gradients. Upon receiving the aggregation result from the server, they collaboratively unlock the commitment, thereby verifying the aggregation result. Security analysis demonstrates that our solution effectively ensures privacy protection. We tested the performance using a Raspberry Pi as an edge computing device. Experimental data reveals that, with 100 clients, our scheme demonstrates that the additional costs for proof generation and verification computations are 39.9% and 34.1% of the existing scheme, respectively, highlighting its lightweight nature. |
| format | Article |
| id | doaj-art-1dffa323e09d405595f792903b14e0ec |
| institution | Kabale University |
| issn | 2948-2992 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Computing |
| spelling | doaj-art-1dffa323e09d405595f792903b14e0ec2025-08-24T11:46:33ZengSpringerDiscover Computing2948-29922025-08-0128113010.1007/s10791-025-09676-1Verifiable secure aggregation scheme for privacy protection in federated learning networksWujun Yao0Tanping Zhou1Yiliang Han2Xiaolin Wang3College of Cryptography Engineering, Engineering University of People’s Armed PoliceCollege of Cryptography Engineering, Engineering University of People’s Armed PoliceCollege of Cryptography Engineering, Engineering University of People’s Armed PoliceBasic Discipline Department, Engineering University of People’s Armed PoliceAbstract Federated learning enables multiple participants to construct a distributed machine learning system coordinated by a server. Most existing solutions assume a semi-honest system, considering each participant to be honest but curious, which does not align with the complex real-world environment. In reality, servers might act maliciously by tampering with or forging aggregation results, which directly threatens the integrity of global models.. To verify the integrity of server aggregation computations while protecting the privacy of clients, this paper introduces a privacy-preserving verifiable secure aggregation scheme for federated learning networks. Initially, we construct a functional reuse private key ring generation algorithm, enabling clients to encrypt and protect their private gradients using the private key ring. Subsequently, leveraging the discrete logarithm difficulty problem, we devise a commitment protocol where clients commit to their encrypted private gradients. Upon receiving the aggregation result from the server, they collaboratively unlock the commitment, thereby verifying the aggregation result. Security analysis demonstrates that our solution effectively ensures privacy protection. We tested the performance using a Raspberry Pi as an edge computing device. Experimental data reveals that, with 100 clients, our scheme demonstrates that the additional costs for proof generation and verification computations are 39.9% and 34.1% of the existing scheme, respectively, highlighting its lightweight nature.https://doi.org/10.1007/s10791-025-09676-1Federated learningVerifiable aggregationPrivacy protectionEdge computing |
| spellingShingle | Wujun Yao Tanping Zhou Yiliang Han Xiaolin Wang Verifiable secure aggregation scheme for privacy protection in federated learning networks Discover Computing Federated learning Verifiable aggregation Privacy protection Edge computing |
| title | Verifiable secure aggregation scheme for privacy protection in federated learning networks |
| title_full | Verifiable secure aggregation scheme for privacy protection in federated learning networks |
| title_fullStr | Verifiable secure aggregation scheme for privacy protection in federated learning networks |
| title_full_unstemmed | Verifiable secure aggregation scheme for privacy protection in federated learning networks |
| title_short | Verifiable secure aggregation scheme for privacy protection in federated learning networks |
| title_sort | verifiable secure aggregation scheme for privacy protection in federated learning networks |
| topic | Federated learning Verifiable aggregation Privacy protection Edge computing |
| url | https://doi.org/10.1007/s10791-025-09676-1 |
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