12 citations · 26 across the 8 of their papers we have counts for
3 papers · 1 filter
Communication-Efficient {Federated} Learning Using Censored Heavy Ball Descent
Yicheng Chen, Rick S. Blum, Brian M. Sadler
Distributed machine learning enables scalability and computational offloading, but requires significant levels of communication. Consequently, communication efficiency in distribut…
Communication Efficient Federated Learning via Ordered ADMM in a Fully Decentralized Setting
Yicheng Chen, Rick S. Blum, Brian M. Sadler
The challenge of communication-efficient distributed optimization has attracted attention in recent years. In this paper, a communication efficient algorithm, called ordering-based…
Distributed Learning With Sparsified Gradient Differences
Yicheng Chen, Rick S. Blum, Martin Takac +1
A very large number of communications are typically required to solve distributed learning tasks, and this critically limits scalability and convergence speed in wireless communica…