30 citations · 32 across the 3 of their papers we have counts for
3 papers
cs.LG2026
A Unified Framework for Gradient Aggregation in Multi-Objective Optimization
Zeou Hu, Kelvin Ho, Yaoliang Yu
Many machine learning problems involve multiple inherent trade-offs that are best addressed by gradient-based multi-objective optimization (MOO) algorithms. Existing methods are of…
cs.LG2021★ 2 cited
An Operator Splitting View of Federated Learning
Saber Malekmohammadi, Kiarash Shaloudegi, Zeou Hu +1
Over the past few years, the federated learning () community has witnessed a proliferation of new algorithms. However, our understating of the theory of…
cs.LG2020★ 30 cited
Federated Learning Meets Multi-objective Optimization
Zeou Hu, Kiarash Shaloudegi, Guojun Zhang +1
Federated learning has emerged as a promising, massively distributed way to train a joint deep model over large amounts of edge devices while keeping private user data strictly on…