Mechanisms That Incentivize Data Sharing in Federated Learning

Abstract

Federated learning is typically considered a beneficial technology which allows multiple agents to collaborate with each other, improve the accuracy of their models, and solve problems which are otherwise too data-intensive / expensive to be solved individually. However, under the expectation that other agents will share their data, rational agents may be tempted to engage in detrimental behavior such as free-riding where they contribute no data but still enjoy an improved model. In this work, we propose a framework to analyze the behavior of such rational data generators. We first show how a naive scheme leads to catastrophic levels of free-riding where the benefits of data sharing are completely eroded. Then, using ideas from contract theory, we introduce accuracy shaping based mechanisms to maximize the amount of data generated by each agent. These provably prevent free-riding without needing any payment mechanism.

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Cite

Text

Karimireddy et al. "Mechanisms That Incentivize Data Sharing in Federated Learning." NeurIPS 2022 Workshops: Federated_Learning, 2022.

Markdown

[Karimireddy et al. "Mechanisms That Incentivize Data Sharing in Federated Learning." NeurIPS 2022 Workshops: Federated_Learning, 2022.](https://mlanthology.org/neuripsw/2022/karimireddy2022neuripsw-mechanisms/)

BibTeX

@inproceedings{karimireddy2022neuripsw-mechanisms,
  title     = {{Mechanisms That Incentivize Data Sharing in Federated Learning}},
  author    = {Karimireddy, Sai Praneeth and Guo, Wenshuo and Jordan, Michael},
  booktitle = {NeurIPS 2022 Workshops: Federated_Learning},
  year      = {2022},
  url       = {https://mlanthology.org/neuripsw/2022/karimireddy2022neuripsw-mechanisms/}
}