Workload Allocation for Distributed Coded Machine Learning: From Offline Model-Based to Online Model-Free
Author:
Affiliation:
1. Dalhousie University,Canada
2. Ericsson Inc.,Canada
3. Activision Blizzard Inc.,USA
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx8/8548628/10574189/10574192.pdf?arnumber=10574192
Reference15 articles.
1. Joint Parameter-and-Bandwidth Allocation for Improving the Efficiency of Partitioned Edge Learning
2. Speeding Up Distributed Machine Learning Using Codes
3. A Comprehensive Survey on Coded Distributed Computing: Fundamentals, Challenges, and Networking Applications
4. Optimal Load Allocation for Coded Distributed Computation in Heterogeneous Clusters
5. Coded Computing for Distributed Machine Learning in Wireless Edge Network
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1. DAS: A DRL-Based Scheme for Workload Allocation and Worker Selection in Distributed Coded Machine Learning;IEEE Internet of Things Journal;2024-08-01
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