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Unified Group Fairness on Federated Learning
[article]
2022
arXiv
pre-print
Federated learning (FL) has emerged as an important machine learning paradigm where a global model is trained based on the private data from distributed clients. However, most of existing FL algorithms cannot guarantee the performance fairness towards different groups because of data distribution shift over groups. In this paper, we formulate the problem of unified group fairness on FL, where the groups can be formed by clients (including existing clients and newly added clients) and sensitive
arXiv:2111.04986v3
fatcat:4y2rv26vgvccfoex3oyvbu5mfe