445 citations · 830 across the 60 of their papers we have counts for
9 papers · 1 filter
Communication and Energy Efficient Slimmable Federated Learning via Superposition Coding and Successive Decoding
Hankyul Baek, Won Joon Yun, Soyi Jung +4
Mobile devices are indispensable sources of big data. Federated learning (FL) has a great potential in exploiting these private data by exchanging locally trained models instead of…
Joint Superposition Coding and Training for Federated Learning over Multi-Width Neural Networks
Hankyul Baek, Won Joon Yun, Yunseok Kwak +5
This paper aims to integrate two synergetic technologies, federated learning (FL) and width-adjustable slimmable neural network (SNN) architectures. FL preserves data privacy by ex…
A Q-Learning-based Approach for Distributed Beam Scheduling in mmWave Networks
Xiang Zhang, Shamik Sarkar, Arupjyoti Bhuyan +2
We consider the problem of distributed downlink beam scheduling and power allocation for millimeter-Wave (mmWave) cellular networks where multiple base stations (BSs) belonging to…
A New Design Framework for Heterogeneous Uncoded Storage Elastic Computing
Mingyue Ji, Xiang Zhang, Kai Wan
Elasticity is one important feature in modern cloud computing systems and can result in computation failure or significantly increase computing time. Such elasticity means that vir…
A Practical Algorithm Design and Evaluation for Heterogeneous Elastic Computing with Stragglers
Nicholas Woolsey, Joerg Kliewer, Rong-Rong Chen +1
Our extensive real measurements over Amazon EC2 show that the virtual instances often have different computing speeds even if they share the same configurations. This motivates us…
Throughput-Outage Scaling Behaviors for Wireless Single-Hop D2D Caching Networks with Physical Model -- Analysis and Derivations
Ming-Chun Lee, Andreas F. Molisch, Mingyue Ji
Throughput-Outage scaling laws for single-hop cache-aided device-to-device (D2D) communications have been extensively investigated under the assumption of the protocol model. Howev…