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Towards Realistic Mechanisms That Incentivize Federated Participation and Contribution

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Listed:
  • Marco Bornstein
  • Amrit Singh Bedi
  • Anit Kumar Sahu
  • Furqan Khan
  • Furong Huang

Abstract

Edge device participation in federating learning (FL) is typically studied through the lens of device-server communication (e.g., device dropout) and assumes an undying desire from edge devices to participate in FL. As a result, current FL frameworks are flawed when implemented in realistic settings, with many encountering the free-rider dilemma. In a step to push FL towards realistic settings, we propose RealFM: the first federated mechanism that (1) realistically models device utility, (2) incentivizes data contribution and device participation, (3) provably removes the free-rider dilemma, and (4) relaxes assumptions on data homogeneity and data sharing. Compared to previous FL mechanisms, RealFM allows for a non-linear relationship between model accuracy and utility, which improves the utility gained by the server and participating devices. On real-world data, RealFM improves device and server utility, as well as data contribution, by over 3 and 4 magnitudes respectively compared to baselines.

Suggested Citation

  • Marco Bornstein & Amrit Singh Bedi & Anit Kumar Sahu & Furqan Khan & Furong Huang, 2023. "Towards Realistic Mechanisms That Incentivize Federated Participation and Contribution," Papers 2310.13681, arXiv.org, revised May 2024.
    • Handle: RePEc:arx:papers:2310.13681
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    File URL: http://arxiv.org/pdf/2310.13681
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    References listed on IDEAS

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    1. Sai Praneeth Karimireddy & Wenshuo Guo & Michael I. Jordan, 2022. "Mechanisms that Incentivize Data Sharing in Federated Learning," Papers 2207.04557, arXiv.org.
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