Nothing Wasted: Full Contribution Enforcement in Federated Edge Learning
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Abstract (arXiv)
The explosive amount of data generated at the network edge makes mobile edge computing an essential technology to support real-time applications, calling for powerful data processing and analysis provided by machine learning (ML) techniques. In particular, federated edge learning (FEL) becomes prominent in securing the privacy of data owners by keeping the data locally used to train ML models. Existing studies on FEL either utilize in-process optimization or remove unqualified participants in advance. In this paper, we enhance the collaboration from all edge devices in FEL to guarantee that the ML model is trained using all available local data to accelerate the learning process. To that aim, we propose a collective extortion (CE) strategy under the imperfect-information multi-player FEL game, which is proved to be effective in helping the server efficiently elicit the full contribution of all devices without worrying about suffering from any economic loss. Technically, our proposed CE strategy extends the classical extortion strategy in controlling the proportionate share of expected utilities for a single opponent to the swiftly homogeneous control over a group of players, which further presents an attractive trait of being impartial for all participants. Moreover, the CE strategy enriches the game theory hierarchy, facilitating a wider application scope of the extortion strategy. Both theoretical analysis and experimental evaluations validate the effectiveness and fairness of our proposed scheme.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 21 of its 42 references that are in the map. First posted on arXiv 15 Oct 2021.
Builds on
21 of 42 references.
References · 21
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- 2019Energy-Efficient Radio Resource Allocation for Federated Edge Learningcited by 39
- 2019Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networkscited by 39
9 more
- 2020An Incentive Mechanism for Federated Learning in Wireless Cellular network: An Auction Approachcited by 39
- 2019A Crowdsourcing Framework for On-Device Federated Learningcited by 32
- 2020Federated Learning for 6G Communications: Challenges, Methods, and Future Directionscited by 32
- 2020Coded Computing for Low-Latency Federated Learning over Wireless Edge Networkscited by 32
- 2020Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approachcited by 29
- 2019Helios: Heterogeneity-Aware Federated Learning with Dynamically Balanced Collaborationcited by 15
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- 2018Payoff Control in the Iterated Prisoner's Dilemmacited by 2
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