207 citations · 833 across the 51 of their papers we have counts for
8 papers · 1 filter
Communication-Efficient Distributed SGD with Compressed Sensing
Yujie Tang, Vikram Ramanathan, Junshan Zhang +1
We consider large scale distributed optimization over a set of edge devices connected to a central server, where the limited communication bandwidth between the server and edge dev…
A New Look at AI-Driven NOMA-F-RANs: Features Extraction, Cooperative Caching, and Cache-Aided Computing
Zhong Yang, Yaru Fu, Yuanwei Liu +2
Non-orthogonal multiple access (NOMA) enabled fog radio access networks (NOMA-F-RANs) have been taken as a promising enabler to release network congestion, reduce delivery latency,…
GROWN: GRow Only When Necessary for Continual Learning
Li Yang, Sen Lin, Junshan Zhang +1
Catastrophic forgetting is a notorious issue in deep learning, referring to the fact that Deep Neural Networks (DNN) could forget the knowledge about earlier tasks when learning ne…
Scenario Generation of Wind Farm Power for Real-Time System Operation
Trevor Werho, Junshan Zhang, Vijay Vittal +3
This work proposes a method of wind farm scenario generation to support real-time optimization tools and presents key findings therein. This work draws upon work from the literatur…
A Game-theoretic Approach Towards Collaborative Coded Computation Offloading
Jer Shyuan Ng, Wei Yang Bryan Lim, Zehui Xiong +5
Coded distributed computing (CDC) has emerged as a promising approach because it enables computation tasks to be carried out in a distributed manner while mitigating straggler effe…
Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approach
Junshan Zhang, Na Li, Mehmet Dedeoglu
We consider a many-to-one wireless architecture for federated learning at the network edge, where multiple edge devices collaboratively train a model using local data. The unreliab…