Federated multi-task learning

V Smith, CK Chiang, M Sanjabi… - Advances in neural …, 2017 - proceedings.neurips.cc
Federated learning poses new statistical and systems challenges in training machine
learning models over distributed networks of devices. In this work, we show that multi-task …

Understanding operational 5G: A first measurement study on its coverage, performance and energy consumption

D Xu, A Zhou, X Zhang, G Wang, X Liu, C An… - Proceedings of the …, 2020 - dl.acm.org
5G, as a monumental shift in cellular communication technology, holds tremendous potential
for spurring innovations across many vertical industries, with its promised multi-Gbps speed …

A first look at commercial 5G performance on smartphones

A Narayanan, E Ramadan, J Carpenter, Q Liu… - Proceedings of The …, 2020 - dl.acm.org
We conduct to our knowledge a first measurement study of commercial 5G performance on
smartphones by closely examining 5G networks of three carriers (two mmWave carriers, one …

Optimal client sampling for federated learning

W Chen, S Horvath, P Richtarik - arxiv preprint arxiv:2010.13723, 2020 - arxiv.org
It is well understood that client-master communication can be a primary bottleneck in
Federated Learning. In this work, we address this issue with a novel client subsampling …

[HTML][HTML] Towards asynchronous federated learning for heterogeneous edge-powered internet of things

Z Chen, W Liao, K Hua, C Lu, W Yu - Digital Communications and Networks, 2021 - Elsevier
The advancement of the Internet of Things (IoT) brings new opportunities for collecting real-
time data and deploying machine learning models. Nonetheless, an individual IoT device …

Omnimon: Re-architecting network telemetry with resource efficiency and full accuracy

Q Huang, H Sun, PPC Lee, W Bai, F Zhu… - Proceedings of the Annual …, 2020 - dl.acm.org
Network telemetry is essential for administrators to monitor massive data traffic in a network-
wide manner. Existing telemetry solutions often face the dilemma between resource …