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...

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Autori principali:	Rahul Haripriya, Amit Kumar, Manish Pandey, Nilay Khare, Jaytrilok Choudhary, Dhirendra Pratap Singh, Surendra Solanki, Akash Haripriya
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	IEEE 2025-01-01
Serie:	IEEE Access
Soggetti:	Federated learning responsible AI edge computing machine learning data mining collaborative AI
Accesso online:	https://ieeexplore.ieee.org/document/11153937/
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