320 citations · 349 across the 4 of their papers we have counts for
4 papers
FedJAX: Federated learning simulation with JAX
Jae Hun Ro, Ananda Theertha Suresh, Ke Wu
Federated learning is a machine learning technique that enables training across decentralized data. Recently, federated learning has become an active area of research due to an inc…
Communication-Efficient Agnostic Federated Averaging
Jae Ro, Mingqing Chen, Rajiv Mathews +2
In distributed learning settings such as federated learning, the training algorithm can be potentially biased towards different clients. Mohri et al. (2019) proposed a domain-agnos…
Semi-supervised URL Segmentation with Recurrent Neural Networks Pre-trained on Knowledge Graph Entities
Hao Zhang, Jae Ro, Richard Sproat
Breaking domain names such as openresearch into component words open and research is important for applications like Text-to-Speech synthesis and web search. We link this problem t…
Three Approaches for Personalization with Applications to Federated Learning
Yishay Mansour, Mehryar Mohri, Jae Ro +1
The standard objective in machine learning is to train a single model for all users. However, in many learning scenarios, such as cloud computing and federated learning, it is poss…