CoSIFL: Collaborative Secure and Incentivized Federated Learning with Differential Privacy
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| Format: | Preprint |
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2025
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| _version_ | 1866908562712690688 |
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| author | Xie, Zhanhong Zhang, Meifan Yin, Lihua |
| author_facet | Xie, Zhanhong Zhang, Meifan Yin, Lihua |
| contents | Federated learning (FL) has emerged as a promising paradigm for collaborative model training while preserving data locality. However, it still faces challenges from malicious or compromised clients, as well as difficulties in incentivizing participants to contribute high-quality data under strict privacy requirements. Motivated by these considerations, we propose CoSIFL, a novel framework that integrates proactive alarming for robust security and local differential privacy (LDP) for inference attacks, together with a Stackelberg-based incentive scheme to encourage client participation and data sharing. Specifically, CoSIFL uses an active alarming mechanism and robust aggregation to defend against Byzantine and inference attacks, while a Tullock contest-inspired incentive module rewards honest clients for both data contributions and reliable alarm triggers. We formulate the interplay between the server and clients as a two-stage game: in the first stage, the server determines total rewards, selects participants, and fixes global iteration settings, whereas in the second stage, each client decides its mini-batch size, privacy noise scale, and alerting strategy. We prove that the server-client game admits a unique equilibrium, and analyze how clients' multi-dimensional attributes - such as non-IID degrees and privacy budgets - jointly affect system efficiency. Experimental results on standard benchmarks demonstrate that CoSIFL outperforms state-of-the-art solutions in improving model robustness and reducing total server costs, highlighting the effectiveness of our integrated design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_23190 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CoSIFL: Collaborative Secure and Incentivized Federated Learning with Differential Privacy Xie, Zhanhong Zhang, Meifan Yin, Lihua Machine Learning Federated learning (FL) has emerged as a promising paradigm for collaborative model training while preserving data locality. However, it still faces challenges from malicious or compromised clients, as well as difficulties in incentivizing participants to contribute high-quality data under strict privacy requirements. Motivated by these considerations, we propose CoSIFL, a novel framework that integrates proactive alarming for robust security and local differential privacy (LDP) for inference attacks, together with a Stackelberg-based incentive scheme to encourage client participation and data sharing. Specifically, CoSIFL uses an active alarming mechanism and robust aggregation to defend against Byzantine and inference attacks, while a Tullock contest-inspired incentive module rewards honest clients for both data contributions and reliable alarm triggers. We formulate the interplay between the server and clients as a two-stage game: in the first stage, the server determines total rewards, selects participants, and fixes global iteration settings, whereas in the second stage, each client decides its mini-batch size, privacy noise scale, and alerting strategy. We prove that the server-client game admits a unique equilibrium, and analyze how clients' multi-dimensional attributes - such as non-IID degrees and privacy budgets - jointly affect system efficiency. Experimental results on standard benchmarks demonstrate that CoSIFL outperforms state-of-the-art solutions in improving model robustness and reducing total server costs, highlighting the effectiveness of our integrated design. |
| title | CoSIFL: Collaborative Secure and Incentivized Federated Learning with Differential Privacy |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.23190 |