575 citations · 909 across the 6 of their papers we have counts for
10 papers
A Field Guide to Federated Optimization
Jianyu Wang, Zachary Charles, Zheng Xu +50
Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy prote…
Local Adaptivity in Federated Learning: Convergence and Consistency
Jianyu Wang, Zheng Xu, Zachary Garrett +3
The federated learning (FL) framework trains a machine learning model using decentralized data stored at edge client devices by periodically aggregating locally trained models. Pop…
Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies
Yae Jee Cho, Jianyu Wang, Gauri Joshi
Federated learning is a distributed optimization paradigm that enables a large number of resource-limited client nodes to cooperatively train a model without data sharing. Several…
Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization
Jianyu Wang, Qinghua Liu, Hao Liang +2
In federated optimization, heterogeneity in the clients' local datasets and computation speeds results in large variations in the number of local updates performed by each client i…
Machine Learning on Volatile Instances
Xiaoxi Zhang, Jianyu Wang, Gauri Joshi +1
Due to the massive size of the neural network models and training datasets used in machine learning today, it is imperative to distribute stochastic gradient descent (SGD) by split…
Overlap Local-SGD: An Algorithmic Approach to Hide Communication Delays in Distributed SGD
Jianyu Wang, Hao Liang, Gauri Joshi
Distributed stochastic gradient descent (SGD) is essential for scaling the machine learning algorithms to a large number of computing nodes. However, the infrastructures variabilit…