Web: http://arxiv.org/abs/2111.01221

June 24, 2022, 1:11 a.m. | Houssem Sifaou, Geoffrey Ye Li

cs.LG updates on arXiv.org arxiv.org

This paper investigates the robustness of over-the-air federated learning to
Byzantine attacks. The simple averaging of the model updates via over-the-air
computation makes the learning task vulnerable to random or intended
modifications of the local model updates of some malicious clients. We propose
a robust transmission and aggregation framework to such attacks while
preserving the benefits of over-the-air computation for federated learning. For
the proposed robust federated learning, the participating clients are randomly
divided into groups and a transmission time …

arxiv computation federated learning learning lg

More from arxiv.org / cs.LG updates on arXiv.org

Machine Learning Researcher - Saalfeld Lab

@ Howard Hughes Medical Institute - Chevy Chase, MD | Ashburn, Virginia

Project Director, Machine Learning in US Health

@ ideas42.org | Remote, US

Data Science Intern

@ NannyML | Remote

Machine Learning Engineer NLP/Speech

@ Play.ht | Remote

Research Scientist, 3D Reconstruction

@ Yembo | Remote, US

Clinical Assistant or Associate Professor of Management Science and Systems

@ University at Buffalo | Buffalo, NY