6 citations · 7 across the 2 of their papers we have counts for
2 papers
cs.LG2022★ 6 cited
Asynchronous Hierarchical Federated Learning
Xing Wang, Yijun Wang
Federated Learning is a rapidly growing area of research and with various benefits and industry applications. Typical federated patterns have some intrinsic issues such as heavy se…
cs.LG2020★ 1 cited
Training Recommender Systems at Scale: Communication-Efficient Model and Data Parallelism
Vipul Gupta, Dhruv Choudhary, Ping Tak Peter Tang +6
In this paper, we consider hybrid parallelism -- a paradigm that employs both Data Parallelism (DP) and Model Parallelism (MP) -- to scale distributed training of large recommendat…