1.2k citations · 9.5k across the 261 of their papers we have counts for
8 papers · 2 filters
Advances and Open Problems in Federated Learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent +56
Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a cen…
L-FGADMM: Layer-Wise Federated Group ADMM for Communication Efficient Decentralized Deep Learning
Anis Elgabli, Jihong Park, Sabbir Ahmed +1
This article proposes a communication-efficient decentralized deep learning algorithm, coined layer-wise federated group ADMM (L-FGADMM). To minimize an empirical risk, every worke…
A Crowdsourcing Framework for On-Device Federated Learning
Shashi Raj Pandey, Nguyen H. Tran, Mehdi Bennis +3
Federated learning (FL) rests on the notion of training a global model in a decentralized manner. Under this setting, mobile devices perform computations on their local data before…
Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning
Anis Elgabli, Jihong Park, Amrit S. Bedi +3
In this article, we propose a communication-efficient decentralized machine learning (ML) algorithm, coined quantized group ADMM (Q-GADMM). To reduce the number of communication li…
GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning
Anis Elgabli, Jihong Park, Amrit S. Bedi +2
When the data is distributed across multiple servers, lowering the communication cost between the servers (or workers) while solving the distributed learning problem is an importan…
Multi-hop Federated Private Data Augmentation with Sample Compression
Eunjeong Jeong, Seungeun Oh, Jihong Park +3
On-device machine learning (ML) has brought about the accessibility to a tremendous amount of data from the users while keeping their local data private instead of storing it in a…