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arXiv · 2310.13681

Towards Realistic Mechanisms That Incentivize Federated Participation and Contribution

Marco Bornstein·Amrit Singh Bedi·Anit Kumar Sahu·Furqan Khan·Furong Huang
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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.

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Marco Bornstein, Amrit Singh Bedi, Anit Kumar Sahu, Furqan Khan, Furong Huang. 2023-10-20. Towards Realistic Mechanisms That Incentivize Federated Participation and Contribution. https://arxiv.org/abs/2310.13681

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