Communication-Balancing Threshold for Event-Triggered Federated Learning
Federated Learning (FL) enables training models across distributed devices while preserving data privacy by avoiding raw data sharing. However, it suffers from significant communication overhead. Event-Triggered FL (ETFL) addresses this issue by allowing devices to transmit updates only when substan...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11096591/ |
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