Digital Twin Enabled Asynchronous SplitFed Learning in E-Healthcare Systems
Author:
Affiliation:
1. School of Computing Technologies, RMIT University, Melbourne, VIC, Australia
2. School of Computer Science, The University of Oklahoma, Norman, OK, USA
Funder
Australian Research Council Discovery Project
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Networks and Communications
Link
http://xplorestaging.ieee.org/ielx7/49/10297955/10234566.pdf?arnumber=10234566
Reference32 articles.
1. Blockchain-Enabled Asynchronous Federated Learning in Edge Computing
2. CE-Fed: Communication efficient multi-party computation enabled federated learning
3. Adaptive Federated Learning and Digital Twin for Industrial Internet of Things
4. FedSA: A staleness-aware asynchronous Federated Learning algorithm with non-IID data
5. A Deep Evolutionary Approach to Bioinspired Classifier Optimisation for Brain-Machine Interaction
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