Fairness Without Demographics in Human-Centered Federated Learning

arXiv:2404.19725v1 Announce Type: new
Abstract: Federated learning (FL) enables collaborative model training while preserving data privacy, making it suitable for decentralized human-centered AI applications. However, a significant research gap remains in ensuring fairness in these systems. Current fairness strategies in FL require knowledge of bias-creating/sensitive attributes, clashing with FL's privacy principles. Moreover, in human-centered datasets, sensitive attributes may remain latent. To tackle these challenges, we present a novel bias mitigation approach inspired by "Fairness without Demographics" in machine learning. The …

abstract ai applications applications arxiv bias collaborative cs.ai cs.dc cs.lg current data data privacy decentralized demographics fairness federated learning gap however human knowledge making privacy research strategies systems training type while