Federated learning over wireless networks: A band-limited coordinated descent approach

J Zhang, N Li, M Dedeoglu - IEEE INFOCOM 2021-IEEE …, 2021 - ieeexplore.ieee.org
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 unreliable …
被引用次数：38 相关文章

Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approach

J Zhang, N Li, M Dedeoglu - arXiv e-prints, 2021 - ui.adsabs.harvard.edu
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 unreliable …

Federated learning over wireless networks: A band-limited coordinated descent approach

J Zhang, N Li, M Dedeoglu - 40th IEEE Conference on …, 2021 - asu.elsevierpure.com
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 unreliable …

Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approach

J Zhang, N Li, M Dedeoglu - arXiv preprint arXiv:2102.07972, 2021 - arxiv.org
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 unreliable …

Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approach

J Zhang, N Li, M Dedeoglu - IEEE INFOCOM 2021-IEEE Conference on …, 2021 - dl.acm.org
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 unreliable …

Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approach

J Zhang, N Li, M Dedeoglu - IEEE INFOCOM 2021-IEEE Conference on …, 2021 - par.nsf.gov
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 unreliable …