most citedAchieving Fairness Across Local and Global Models in Federated Learning

3 citations · 5 across the 2 of their papers we have counts for

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cs.LG2024★ 3 cited

Achieving Fairness Across Local and Global Models in Federated Learning

Disha Makhija, Xing Han, Joydeep Ghosh +1

Achieving fairness across diverse clients in Federated Learning (FL) remains a significant challenge due to the heterogeneity of the data and the inaccessibility of sensitive attri…

cs.LG2024★ 2 cited

Federated Learning for Estimating Heterogeneous Treatment Effects

Disha Makhija, Joydeep Ghosh, Yejin Kim

Machine learning methods for estimating heterogeneous treatment effects (HTE) facilitate large-scale personalized decision-making across various domains such as healthcare, policy…