6 papers
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…
Multi-UAV Mobile Edge Computing and Path Planning Platform based on Reinforcement Learning
Huan Chang, Yicheng Chen, Baochang Zhang +1
Unmanned Aerial vehicles (UAVs) are widely used as network processors in mobile networks, but more recently, UAVs have been used in Mobile Edge Computing as mobile servers. However…
Testing the Structure of a Gaussian Graphical Model with Reduced Transmissions in a Distributed Setting
Yicheng Chen, Rick S. Blum, Brian M. Sadler +1
Testing a covariance matrix following a Gaussian graphical model (GGM) is considered in this paper based on observations made at a set of distributed sensors grouped into clusters.…
On the Impact of Unknown Signals in Passive Radar with Direct Path and Reflected Path Observations
Yicheng Chen, Rick S. Blum
We derive the closed form Cramer-Rao bound (CRB) expressions for joint estimation of time delay and Doppler shift with unknown signals with possibly known structure. The results ar…