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Mechanism Design for Federated Learning with Non-Monotonic Network Effects

arXiv 2601.04648 ↗First posted 8 Jan 2026cs.GT cs.DC cs.LGcited by 0

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Abstract (arXiv)

Mechanism design is pivotal to federated learning (FL) for maximizing social welfare by coordinating self-interested clients. Existing mechanisms, however, often overlook the network effects of client participation and the diverse model performance requirements (i.e., generalization error) across applications, leading to suboptimal incentives and social welfare, or even inapplicability in real deployments. To address this gap, we explore incentive mechanism design for FL with network effects and application-specific requirements of model performance. We develop a theoretical model to quantify the impact of network effects on heterogeneous client participation, revealing the non-monotonic nature of such effects. Based on these insights, we propose a Model Trading and Sharing (MoTS) framework, which enables clients to obtain FL models through either participation or purchase. To further address clients' strategic behaviors, we design a Social Welfare maximization with Application-aware and Network effects (SWAN) mechanism, exploiting model customer payments for incentivization. Experimental results on a hardware prototype demonstrate that our SWAN mechanism outperforms existing FL mechanisms, improving social welfare by up to 352.42% and reducing extra incentive costs by 93.07%.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 30 of its 72 references that are in the map. First posted on arXiv 8 Jan 2026.

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References · 30

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  3. 2017Federated Multi-Task Learningcited by 669
  4. 2017Deep Learning Scaling is Predictable, Empiricallycited by 556
  5. 2019Surprises in High-Dimensional Ridgeless Least Squares Interpolationcited by 508
  6. 2021Towards Personalized Federated Learningcited by 462
  7. 2019The generalization error of random features regression: Precise asymptotics and double descent curvecited by 410
  8. 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 322
  9. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 247
  10. 2021Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learningcited by 192
  11. 2021Federated Learning for Smart Healthcare: A Surveycited by 182
  12. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 97
18 more
  1. 2018A Marketplace for Data: An Algorithmic Solutioncited by 75
  2. 2019Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanismcited by 75
  3. 2020Cost-Effective Federated Learning Designcited by 61
  4. 2021Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspectivecited by 47
  5. 2023Recent Advances on Federated Learning: A Systematic Surveycited by 40
  6. 2020An Incentive Mechanism for Federated Learning in Wireless Cellular network: An Auction Approachcited by 39
  7. 2019Toward an Automated Auction Framework for Wireless Federated Learning Services Marketcited by 38
  8. 2021A Comprehensive Survey of Incentive Mechanism for Federated Learningcited by 38
  9. 2022Mechanisms that Incentivize Data Sharing in Federated Learningcited by 38
  10. 2018Model-based Pricing for Machine Learning in a Data Marketplacecited by 37
  11. 2023Federated Learning under Heterogeneous and Correlated Client Availabilitycited by 18
  12. 2021Data Pricing in Machine Learning Pipelinescited by 17
  13. 2023Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participationcited by 8
  14. 2021Dynamic Federated Learning-Based Economic Framework for Internet-of-Vehiclescited by 7
  15. 2023Truthful Incentive Mechanism for Federated Learning with Crowdsourced Data Labelingcited by 4
  16. 2025Optimizing Communication and Device Clustering for Clustered Federated Learning with Differential Privacycited by 4
  17. 2023Federated Learning as a Network Effects Gamecited by 1
  18. 2024Social Welfare Maximization for Federated Learning with Network Effectscited by 1

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