DualGFL: Federated Learning with a Dual-Level Coalition-Auction Game
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915072850264064 |
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| author | Chen, Xiaobing Zhou, Xiangwei Zhang, Songyang Sun, Mingxuan |
| author_facet | Chen, Xiaobing Zhou, Xiangwei Zhang, Songyang Sun, Mingxuan |
| contents | Despite some promising results in federated learning using game-theoretical methods, most existing studies mainly employ a one-level game in either a cooperative or competitive environment, failing to capture the complex dynamics among participants in practice. To address this issue, we propose DualGFL, a novel Federated Learning framework with a Dual-level Game in cooperative-competitive environments. DualGFL includes a lower-level hedonic game where clients form coalitions and an upper-level multi-attribute auction game where coalitions bid for training participation. At the lower-level DualGFL, we introduce a new auction-aware utility function and propose a Pareto-optimal partitioning algorithm to find a Pareto-optimal partition based on clients' preference profiles. At the upper-level DualGFL, we formulate a multi-attribute auction game with resource constraints and derive equilibrium bids to maximize coalitions' winning probabilities and profits. A greedy algorithm is proposed to maximize the utility of the central server. Extensive experiments on real-world datasets demonstrate DualGFL's effectiveness in improving both server utility and client utility. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_15492 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DualGFL: Federated Learning with a Dual-Level Coalition-Auction Game Chen, Xiaobing Zhou, Xiangwei Zhang, Songyang Sun, Mingxuan Computer Science and Game Theory Machine Learning I.2.6; I.2.11 Despite some promising results in federated learning using game-theoretical methods, most existing studies mainly employ a one-level game in either a cooperative or competitive environment, failing to capture the complex dynamics among participants in practice. To address this issue, we propose DualGFL, a novel Federated Learning framework with a Dual-level Game in cooperative-competitive environments. DualGFL includes a lower-level hedonic game where clients form coalitions and an upper-level multi-attribute auction game where coalitions bid for training participation. At the lower-level DualGFL, we introduce a new auction-aware utility function and propose a Pareto-optimal partitioning algorithm to find a Pareto-optimal partition based on clients' preference profiles. At the upper-level DualGFL, we formulate a multi-attribute auction game with resource constraints and derive equilibrium bids to maximize coalitions' winning probabilities and profits. A greedy algorithm is proposed to maximize the utility of the central server. Extensive experiments on real-world datasets demonstrate DualGFL's effectiveness in improving both server utility and client utility. |
| title | DualGFL: Federated Learning with a Dual-Level Coalition-Auction Game |
| topic | Computer Science and Game Theory Machine Learning I.2.6; I.2.11 |
| url | https://arxiv.org/abs/2412.15492 |