activity
20202022
most citedA Field Guide to Federated Optimization

167 citations · 344 across the 6 of their papers we have counts for

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Showing cs.LGShow all

5 papers · 1 filter

cs.LG2022

Smartphone-based Hard-braking Event Detection at Scale for Road Safety Services

Luyang Liu, David Racz, Kara Vaillancourt +9

Road crashes are the sixth leading cause of lost disability-adjusted life-years (DALYs) worldwide. One major challenge in traffic safety research is the sparsity of crashes, which…

cs.LG2021167 cited

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…

cs.LG20215 cited

Byzantine-robust Federated Learning through Spatial-temporal Analysis of Local Model Updates

Zhuohang Li, Luyang Liu, Jiaxin Zhang +1

Federated Learning (FL) enables multiple distributed clients (e.g., mobile devices) to collaboratively train a centralized model while keeping the training data locally on the clie…

cs.LG202121 cited

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…

cs.LG2020150 cited

Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks

Amol Kapoor, Xue Ben, Luyang Liu +4

In this work, we examine a novel forecasting approach for COVID-19 case prediction that uses Graph Neural Networks and mobility data. In contrast to existing time series forecastin…