mlsanityData to 14 Oct 2026

Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach

arXiv 1905.07479 ↗First posted 16 May 2019cs.LG cs.GT cs.NIcited by 45

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Abstract (arXiv)

To strengthen data privacy and security, federated learning as an emerging machine learning technique is proposed to enable large-scale nodes, e.g., mobile devices, to distributedly train and globally share models without revealing their local data. This technique can not only significantly improve privacy protection for mobile devices, but also ensure good performance of the trained results collectively. Currently, most the existing studies focus on optimizing federated learning algorithms to improve model training performance. However, incentive mechanisms to motivate the mobile devices to join model training have been largely overlooked. The mobile devices suffer from considerable overhead in terms of computation and communication during the federated model training process. Without well-designed incentive, self-interested mobile devices will be unwilling to join federated learning tasks, which hinders the adoption of federated learning. To bridge this gap, in this paper, we adopt the contract theory to design an effective incentive mechanism for simulating the mobile devices with high-quality (i.e., high-accuracy) data to participate in federated learning. Numerical results demonstrate that the proposed mechanism is efficient for federated learning with improved learning accuracy.

Key facts. Cited by 45 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 May 2019.

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Citing papers per year: 2021: 14; 2022: 13; 2023: 7; 2024: 3; 2025: 6; 2026: 2.

45 papers.

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  1. 2021Federated Learning: Opportunities and Challengescited by 84
  2. 2021Towards Fairness-Aware Federated Learningcited by 58
  3. 2021Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspectivecited by 47
  4. 2022Mechanisms that Incentivize Data Sharing in Federated Learningcited by 39
  5. 2021A Comprehensive Survey of Incentive Mechanism for Federated Learningcited by 38
  6. 2021Game of Gradients: Mitigating Irrelevant Clients in Federated Learningcited by 35
  7. 2023A Survey on Decentralized Federated Learningcited by 27
  8. 2021Federated Learning on the Road: Autonomous Controller Design for Connected and Autonomous Vehiclescited by 26
  9. 2021Incentivizing Collaboration in Machine Learning via Synthetic Data Rewardscited by 20
  10. 2021FedH2L: Federated Learning with Model and Statistical Heterogeneitycited by 17
  11. 2021A Contract Theory based Incentive Mechanism for Federated Learningcited by 13
  12. 2024AdaFed: Fair Federated Learning via Adaptive Common Descent Directioncited by 13
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  1. 2021Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networkscited by 10
  2. 2021FL-Market: Trading Private Models in Federated Learningcited by 9
  3. 2021Dynamic Attention-based Communication-Efficient Federated Learningcited by 9
  4. 2022Collaboration in Participant-Centric Federated Learning: A Game-Theoretical Perspectivecited by 9
  5. 2022FedToken: Tokenized Incentives for Data Contribution in Federated Learningcited by 9
  6. 2022A Snapshot of the Frontiers of Client Selection in Federated Learningcited by 9
  7. 2021On Addressing Heterogeneity in Federated Learning for Autonomous Vehicles Connected to a Drone Orchestratorcited by 6
  8. 2022FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Datacited by 6
  9. 2022Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Designcited by 6
  10. 2023FedDebug: Systematic Debugging for Federated Learning Applicationscited by 6
  11. 2025Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networkscited by 6
  12. 2022Incentivizing Federated Learningcited by 5
  13. 2025Distributed Quasi-Newton Method for Fair and Fast Federated Learningcited by 5
  14. 2025PQS-BFL: A Post-Quantum Secure Blockchain-based Federated Learning Frameworkcited by 4
  15. 2022Maximizing Global Model Appeal in Federated Learningcited by 3
  16. 2021Reward-Based 1-bit Compressed Federated Distillation on Blockchaincited by 2
  17. 2023A Framework for Incentivized Collaborative Learningcited by 2
  18. 2022eFedDNN: Ensemble based Federated Deep Neural Networks for Trajectory Mode Inferencecited by 1
  19. 2022DP2-NILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoringcited by 1
  20. 2022Reducing Training Time in Cross-Silo Federated Learning using Multigraph Topologycited by 1
  21. 2022Scheduling Algorithms for Federated Learning with Minimal Energy Consumptioncited by 1
  22. 2023Welfare and Fairness Dynamics in Federated Learning: A Client Selection Perspectivecited by 1
  23. 2023Learner Referral for Cost-Effective Federated Learning Over Hierarchical IoT Networkscited by 1
  24. 2023Cross-Silo Federated Learning Across Divergent Domains with Iterative Parameter Alignmentcited by 1
  25. 2024Acoustic Model Optimization over Multiple Data Sources: Merging and Valuationcited by 1
  26. 2022Distributed Stochastic Gradient Descent with Cost-Sensitive and Strategic Agentscited by 0
  27. 2023Blockchain-based Optimized Client Selection and Privacy Preserved Framework for Federated Learningcited by 0
  28. 2024Paid with Models: Optimal Contract Design for Collaborative Machine Learningcited by 0
  29. 2025ATR-Bench: A Federated Learning Benchmark for Adaptation, Trust, and Reasoningcited by 0
  30. 2025Incentivizing High-quality Participation From Federated Learning Agentscited by 0
  31. 2025Long-Term Client Selection for Federated Learning with Non-IID Data: A Truthful Auction Approachcited by 0
  32. 2026Unextractable Protocol Models: Collaborative Training and Inference without Weight Materializationcited by 0
  33. 2026A Game-Theoretic Framework for Incentive-Compatible AI training Under Renewable-Energy Constraintscited by 0