Optimizing Fault Tolerance and Latency of Federated Learning Using Edge Servers and Pre-Trained Model
Federated learning (FL) presents a promising paradigm for decentralized machine learning, particularly well-suited for data-sensitive cyber-physical systems (CPS) where privacy preservation and low-latency inference are paramount. However, FL deployments at the edge face acute challenges in fault to...
| Published in: | IEEE Access |
|---|---|
| Main Authors: | , , , , , , , |
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11153937/ |
