A Verifiable Federated Learning Scheme Based on Homomorphic Encryption and Group Signature

LI Yahong; LI Yijing; YANG Xiaodong; ZHANG Yuan; NIU Shufen

doi:10.11999/JEIT240796

Volume 47 Issue 3

Mar. 2025

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Article Navigation > Journal of Electronics & Information Technology > 2025 > 47(3): 758-768

LI Yahong, LI Yijing, YANG Xiaodong, ZHANG Yuan, NIU Shufen. A Verifiable Federated Learning Scheme Based on Homomorphic Encryption and Group Signature[J]. Journal of Electronics & Information Technology, 2025, 47(3): 758-768. doi: 10.11999/JEIT240796

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LI Yahong, LI Yijing, YANG Xiaodong, ZHANG Yuan, NIU Shufen. A Verifiable Federated Learning Scheme Based on Homomorphic Encryption and Group Signature[J]. Journal of Electronics & Information Technology, 2025, 47(3): 758-768. doi: 10.11999/JEIT240796

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A Verifiable Federated Learning Scheme Based on Homomorphic Encryption and Group Signature

doi: 10.11999/JEIT240796 cstr: 32379.14.JEIT240796

1.
School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730000, China
2.
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chendu 610054, China
3.
School of Computer Science and Engineering, Northwestern Normal University, Lanzhou 730000, China

Funds: The National Natural Science Foundation of China (62461032), Gansu Science and Technology Plan (22JR5RA158, 22JR5RA350), Gansu Province University Teachers Innovation Fund Project (2023A-041, 2023-ZD-234), Lanzhou Jiaotong University-Tianjin University Joint Innovation Fund Project (LH2024003)

Received Date: 2024-09-14
Rev Recd Date: 2025-02-17

Available Online: 2025-02-21

Publish Date: 2025-03-01

Abstract

Abstract

Objective In Vehicular Ad-hoc NETworks (VANETs), network instability and frequent vehicle mobility complicate data aggregation and expose it to potential attacks. Traditional Federated Learning (FL) approaches face challenges such as high computational and communication overheads, insufficient privacy protection, and difficulties in verifying aggregation results, which impact model training efficiency and stability. To address these issues, this study proposes a scheme that integrates the Boneh-Lynn-Shacham (BLS) dynamic short group signature with an enhanced Cheon-Kim-Kim-Song (CKKS) homomorphic encryption technique. This approach reduces computational and communication costs, ensures data privacy under chosen-plaintext attacks, and maintains system stability by allowing vehicles to disconnect after submitting encrypted data. The proposed framework enhances privacy, verifiability, anonymity, traceability, and robustness, providing a secure and reliable FL solution for VANETs. Methods A batch aggregation scheme is proposed, integrating an improved CKKS linearly homomorphic encryption algorithm with a BLS-based dynamic short group signature technique to address key challenges in applying FL within VANETs. The improved CKKS linearly homomorphic encryption algorithm mitigates privacy leakage risks in vehicle data and training models. Data security and training privacy are ensured by maintaining ciphertext indistinguishability under chosen-plaintext attacks, preventing attackers from inferring original data from ciphertext and protecting vehicle users’ privacy. Linearly homomorphic hashing verifies aggregation result correctness while reducing computational load. This approach also allows vehicles to disconnect after submitting encrypted data, enhancing system robustness and stability. Consequently, model training continuity and reliability are maintained even in dynamic and unstable vehicular network conditions. The BLS-based dynamic short group signature technique simplifies group signature generation, improving aggregation efficiency and reducing computational costs. Combined with batch processing of gradient updates, this method significantly lowers computational and communication overhead on the aggregation server. These techniques collectively enhance system efficiency and ensure adaptability to resource-constrained vehicular environments, providing a practical and effective FL solution for VANETs. Results and Discussions The proposed scheme significantly enhances computational efficiency, reduces communication overhead, improves privacy protection, and ensures system stability in FL for vehicular networks. In terms of computational overhead, client-side computation is reduced by an average of 13.5% and 53.6%, while the aggregation server’s computational cost decreases by 42.4% and 33.8%, respectively (Fig. 2a, Fig. 2b), demonstrating the scheme’s ability to efficiently manage large-scale client environments with minimal computational burden. Communication overhead is also significantly minimized as the number of clients increases. By transmitting only masked gradients and hash values, the scheme achieves reductions of 70.7% and 66.8% compared to existing methods, streamlining the aggregation process and eliminating unnecessary data transmission (Fig. 3). This design ensures applicability in resource-constrained vehicular networks. The scheme maintains strong privacy protection, even under increasing noise accumulation. Experimental results confirm that data privacy is safeguarded during training, mitigating the risk of leakage (Table 4). Stability is further demonstrated as the aggregation server’s performance remains unaffected by client dropouts, regardless of dropout ratios or the scale of disconnections. Its non-interactive design allows vehicles to go offline after submitting encrypted gradients, enabling the system to function reliably and maintain stable performance in dynamic vehicular environments (Fig. 4). This feature is particularly critical in scenarios involving unstable network conditions or fluctuating client availability. Furthermore, the scheme achieves a convergence rate exceeding 95% within 15 training rounds (Fig. 5). This rapid convergence is facilitated by the improved CKKS homomorphic encryption algorithm, which supports floating-point operations and enhances the precision of gradient updates. By improving gradient accuracy, the scheme enables efficient and stable model training, even in dynamic network conditions. Collectively, these results demonstrate the scheme’s ability to address critical challenges in FL for VANETs. Conclusions The FL batch aggregation scheme proposed in this study addresses data privacy and security challenges in VANETs. By integrating the BLS dynamic short group signature technique with an improved CKKS linearly homomorphic hashing algorithm, data integrity is preserved during interactions between clients and RoadSide Units (RSUs). The confidentiality and accuracy of gradient aggregation results are ensured, effectively preventing model training failures due to potential data tampering on the server side. The scheme also supports model updates despite vehicle disconnections, enhancing system stability. Experimental results demonstrate improvements in data privacy, security, and result verifiability while maintaining high efficiency. Additionally, it achieves low communication costs and reduced computation time as the number of clients increases, demonstrating strong scalability and practicality.
- Privacy preservation,
- Federated Learning (FL),
- Vehicular Ad-hoc NETworks (VANETs),
- Verifiable aggregation,
- Group signature

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References(17)

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