Fair Federated Learning for Heterogeneous Face Data

• URL: http://arxiv.org/abs/2109.02351v1
• Date: Mon, 6 Sep 2021 10:44:16 GMT
• Title: Fair Federated Learning for Heterogeneous Face Data
• Authors: Samhita Kanaparthy, Manisha Padala, Sankarshan Damle, Sujit Gujar
• Abstract summary: We consider the problem of achieving fair classification in Federated Learning (FL) under data heterogeneity. Most of the approaches proposed for fair classification require diverse data that represent the different demographic groups involved. In contrast, it is common for each client to own data that represents only a single demographic group.
• Score: 10.707311210901548
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We consider the problem of achieving fair classification in Federated Learning (FL) under data heterogeneity. Most of the approaches proposed for fair classification require diverse data that represent the different demographic groups involved. In contrast, it is common for each client to own data that represents only a single demographic group. Hence the existing approaches cannot be adopted for fair classification models at the client level. To resolve this challenge, we propose several aggregation techniques. We empirically validate these techniques by comparing the resulting fairness metrics and accuracy on CelebA, UTK, and FairFace datasets.

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