Survey on incentive-driven federated learning: privacy and security
Federated learning was enabled to allow multiple data holders to jointly complete machine learning tasks without disclosing local data. Incentivizing participants to engage in federated learning and contribute high-quality data was identified as one of the key factors for its success. However, feder...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
POSTS&TELECOM PRESS Co., LTD
2025-06-01
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| Series: | 网络与信息安全学报 |
| Subjects: | |
| Online Access: | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2025035 |
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| Summary: | Federated learning was enabled to allow multiple data holders to jointly complete machine learning tasks without disclosing local data. Incentivizing participants to engage in federated learning and contribute high-quality data was identified as one of the key factors for its success. However, federated learning was found to face challenges in both privacy leakage and security threats in practice. On the one hand, malicious participants were observed to launch active attacks to disrupt the process and results of federated learning, compromising its effectiveness and robustness. On the other hand, privacy leakage risks caused by passive attacks were shown to negatively impact participants’ willingness to join federated learning, making the implementation of incentive mechanisms more difficult. In recent years, extensive research was conducted by the community on incentive-driven federated learning from the perspectives of privacy preservation and security defense, aiming to provide comprehensive solutions that balance security and fairness for federated learning. First, various passive and active attacks faced by incentive-driven federated learning were introduced, and the privacy leakage risks and security threats posed by these attacks were analyzed. Subsequently, a comprehensive review and analysis of incentive-driven federated learning research were provided from the perspectives of privacy preservation and security defense. In terms of privacy preservation, the application of differential privacy and homomorphic encryption technologies in incentive-driven federated learning was focused on. In terms of security defense, various methods that combine incentive mechanisms to defend against poisoning attacks and free-riding attacks were specifically outlined. Finally, the challenges still faced by incentive-driven federated learning in addressing privacy leakage and security attacks were discussed, and potential future research directions in this field were explored. |
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| ISSN: | 2096-109X |