Ding et al., 2022 - Google Patents
Towards an efficient client selection system for federated learningDing et al., 2022
- Document ID
- 7005343542406357547
- Author
- Ding G
- Li Z
- Wu Y
- Yang X
- Aliasgari M
- Xu H
- Publication year
- Publication venue
- International Conference on Cloud Computing
External Links
Snippet
Federated learning is a popular distributed machine learning model where a centralized server orchestrates many distributed clients to coordinate the completion of model training or evaluation without sharing private or local data. More and more modern data applications …
- 238000011156 evaluation 0 abstract description 6
Classifications
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- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5061—Partitioning or combining of resources
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- G06F9/5083—Techniques for rebalancing the load in a distributed system
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- G06F9/505—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals considering the load
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- G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
- G06F9/485—Task life-cycle, e.g. stopping, restarting, resuming execution
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