mlsanityData to 7 Oct 2026

An Incentive Mechanism for Federated Learning in Wireless Cellular network: An Auction Approach

arXiv 2009.10269 ↗First posted 22 Sep 2020cs.LG cs.GT cs.NIcited by 39

Not on the map. Why?

Abstract (arXiv)

Federated Learning (FL) is a distributed learning framework that can deal with the distributed issue in machine learning and still guarantee high learning performance. However, it is impractical that all users will sacrifice their resources to join the FL algorithm. This motivates us to study the incentive mechanism design for FL. In this paper, we consider a FL system that involves one base station (BS) and multiple mobile users. The mobile users use their own data to train the local machine learning model, and then send the trained models to the BS, which generates the initial model, collects local models and constructs the global model. Then, we formulate the incentive mechanism between the BS and mobile users as an auction game where the BS is an auctioneer and the mobile users are the sellers. In the proposed game, each mobile user submits its bids according to the minimal energy cost that the mobile users experiences in participating in FL. To decide winners in the auction and maximize social welfare, we propose the primal-dual greedy auction mechanism. The proposed mechanism can guarantee three economic properties, namely, truthfulness, individual rationality and efficiency. Finally, numerical results are shown to demonstrate the performance effectiveness of our proposed mechanism.

Key facts. Cited by 39 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Sep 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 5; 2023: 10; 2024: 8; 2025: 6; 2026: 4.

39 papers.

Sort:

  1. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 97
  2. 2021Towards Fairness-Aware Federated Learningcited by 57
  3. 2021Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspectivecited by 47
  4. 2021Optimality and Stability in Federated Learning: A Game-theoretic Approachcited by 40
  5. 2024Advances and Open Challenges in Federated Foundation Modelscited by 33
  6. 2023Machine Learning for Large-Scale Optimization in 6G Wireless Networkscited by 18
  7. 2024AdaFed: Fair Federated Learning via Adaptive Common Descent Directioncited by 12
  8. 2022FedToken: Tokenized Incentives for Data Contribution in Federated Learningcited by 9
  9. 2022Collaboration in Participant-Centric Federated Learning: A Game-Theoretical Perspectivecited by 8
  10. 2023Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participationcited by 8
  11. 2023Utility-Maximizing Bidding Strategy for Data Consumers in Auction-based Federated Learningcited by 7
  12. 2022Online Auction-Based Incentive Mechanism Design for Horizontal Federated Learning with Budget Constraintcited by 6
27 more
  1. 2023FedDebug: Systematic Debugging for Federated Learning Applicationscited by 6
  2. 2022Auction-Based Ex-Post-Payment Incentive Mechanism Design for Horizontal Federated Learning with Reputation and Contribution Measurementcited by 5
  3. 2021Federated Learning: Issues in Medical Applicationcited by 4
  4. 2023Trustworthy Federated Learning: A Surveycited by 4
  5. 2024Intelligent Agents for Auction-based Federated Learning: A Surveycited by 4
  6. 2025Distributed Quasi-Newton Method for Fair and Fast Federated Learningcited by 4
  7. 2022Strategic Coalition for Data Pricing in IoT Data Marketscited by 3
  8. 2023Multi-Session Budget Optimization for Forward Auction-based Federated Learningcited by 3
  9. 2024IMFL-AIGC: Incentive Mechanism Design for Federated Learning Empowered by Artificial Intelligence Generated Contentcited by 3
  10. 2023Incentive Allocation in Vertical Federated Learning Based on Bankruptcy Problemcited by 2
  11. 2024How to Collaborate: Towards Maximizing the Generalization Performance in Cross-Silo Federated Learningcited by 2
  12. 2024Incentivized Truthful Communication for Federated Banditscited by 2
  13. 2021Motivating Learners in Multi-Orchestrator Mobile Edge Learning: A Stackelberg Game Approachcited by 1
  14. 2021Nothing Wasted: Full Contribution Enforcement in Federated Edge Learningcited by 1
  15. 2023Price-Discrimination Game for Distributed Resource Management in Federated Learningcited by 1
  16. 2023Federated Learning Incentive Mechanism under Buyers' Auction Marketcited by 1
  17. 2024Acoustic Model Optimization over Multiple Data Sources: Merging and Valuationcited by 1
  18. 2025Incentivizing Multi-Tenant Split Federated Learning for Foundation Models at the Network Edgecited by 1
  19. 2025OPUS-VFL: Incentivizing Optimal Privacy-Utility Tradeoffs in Vertical Federated Learningcited by 1
  20. 2024GPFL: A Gradient Projection-Based Client Selection Framework for Efficient Federated Learningcited by 0
  21. 2025CARE: Compatibility-Aware Incentive Mechanisms for Federated Learning with Budgeted Requesterscited by 0
  22. 2025ATR-Bench: A Federated Learning Benchmark for Adaptation, Trust, and Reasoningcited by 0
  23. 2025Gaming and Cooperation in Federated Learning: What Can Happen and How to Monitor Itcited by 0
  24. 2026Mechanism Design for Federated Learning with Non-Monotonic Network Effectscited by 0
  25. 2026Toward a Sustainable Federated Learning Ecosystem: A Practical Least Core Mechanism for Payoff Allocationcited by 0
  26. 2026Stable and Budget-Feasible Coalition Formation for Clustered Federated Learning: A Hedonic Potential-Game Approachcited by 0
  27. 2026A Game-Theoretic Framework for Incentive-Compatible AI training Under Renewable-Energy Constraintscited by 0