Tan, S., Cheng, H., Wu, X., Yu, H., He, T., Ong, Y. S., Wang, C., & Tao, X. (2024). FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participants. Proceedings of the AAAI Conference on Artificial Intelligence, 38(14), 15231–15239. https://doi.org/10.1609/aaai.v38i14.29446
Abstract:
Federated learning (FL) provides a privacy-preserving approach for collaborative training of machine learning models. Given the potential data heterogeneity, it is crucial to select appropriate collaborators for each FL participant (FL-PT) based on data complementarity. Recent studies have addressed this challenge. Similarly, it is imperative to consider the inter-individual relationships among FL-PTs where some FL-PTs engage in competition. Although FL literature has acknowledged the significance of this scenario, practical methods for establishing FL ecosystems remain largely unexplored. In this paper, we extend a principle from the balance theory, namely “the friend of my enemy is my enemy”, to ensure the absence of conflicting interests within an FL ecosystem. The extended principle and the resulting problem are formulated via graph theory and integer linear programming. A polynomial-time algorithm is proposed to determine the collaborators of each FL-PT. The solution guarantees high scalability, allowing even competing FL-PTs to smoothly join the ecosystem without conflict of interest. The proposed framework jointly considers competition and data heterogeneity. Extensive experiments on real-world and synthetic data demonstrate its efficacy compared to five alternative approaches, and its ability to establish efficient collaboration networks among FL-PTs.
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Funding Info:
This research was supported in part by the National Key R&D Program of China (No. 2022YFB2902900). This research/project is also supported, in part, by the National Research Foundation Singapore and DSO National Laboratories under the AI Singapore Programme (AISG Award No: AISG2-RP-2020-019); by A*STAR under the RIE 2020 Advanced Manufacturing and Engineering (AME) Programmatic Fund (No. A20G8b0102), Singapore; by A*STAR under the A*STAR RIE2025 Manufacturing, Trade and Connectivity (MTC) Industry Alignment Fund- Pre-Positioning (IAF-PP) (No. M23L4a0001), Singapore; and the Center for Frontier AI Research (CFAR), Agency for Science, Technology and Research (A∗STAR), Singapore. The work of Hao Cheng and Chongjun Wang was supported by the National Natural Science Foundation of China (Grant No. 62192783, 62376117). The work of Shanli Tan was done when he was a research intern with Xiaohu Wu at the National Engineering Research Center of Mobile Network Technologies, Beijing University of Posts and Telecommunications, China.