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Survey of Bias Mitigation in Federated Learning

Abstract : Federated Learning (FL) interestingly allows a set of participants to collectively resolve a machine learning problem in a decentralized and privacy preserving manner. However, data distribution and heterogeneity, that are inherent to FL, may induce and exacerbate the problem of bias, with its prejudicial consequences such as racial or sexist segregation, illegal actions, or reduced revenues. In this paper, we describe the problem of bias in Federated Learning, and provide a comparative review of existing approaches of FL bias mitigation, before discussing open challenges and interesting research directions.
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https://hal.archives-ouvertes.fr/hal-03343288
Contributor : Yasmine Djebrouni Connect in order to contact the contributor
Submitted on : Wednesday, September 15, 2021 - 2:23:24 PM
Last modification on : Tuesday, October 19, 2021 - 11:19:00 AM

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  • HAL Id : hal-03343288, version 1
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Lynda Ferraguig, Yasmine Djebrouni, Sara Bouchenak, Vania Marangozova. Survey of Bias Mitigation in Federated Learning. Conférence francophone d'informatique en Parallélisme, Architecture et Système, Jul 2021, Lyon (virtuel), France. ⟨hal-03343288⟩

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