How Asynchronous can Federated Learning Be?-Reference-Cited by-同舟云学术

How Asynchronous can Federated Learning Be?

Published:2022-06-10 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS)
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Author:

Su Ningxin¹,Li Baochun¹

Affiliation:

1. University of Toronto,Department of Electrical and Computer Engineering

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9812862/9812863/09812885.pdf?arnumber=9812885

Reference10 articles.

1. Communication-Efficient Federated Deep Learning With Layerwise Asynchronous Model Update and Temporally Weighted Aggregation

2. More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server;ho;Proc Advances in Neural Information Processing Systems (NeurIPS),2013

3. FedML: A Research Library and Benchmark for Federated Machine Learning;he;Proc NeurIPS Federated Learning Workshop,2020

4. Asynchronous Online Federated Learning for Edge Devices with Non-IID Data

5. FedSA: A staleness-aware asynchronous Federated Learning algorithm with non-IID data

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