3 citations · 3 across the 1 of their papers we have counts for
2 papers
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
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…