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...

Ful tanımlama

Kaydedildi:

Detaylı Bibliyografya
Asıl Yazarlar:	Rahul Haripriya, Amit Kumar, Manish Pandey, Nilay Khare, Jaytrilok Choudhary, Dhirendra Pratap Singh, Surendra Solanki, Akash Haripriya
Materyal Türü:	Artigo
Dil:	Inglês
Baskı/Yayın Bilgisi:	IEEE 2025-01-01
Seri Bilgileri:	IEEE Access
Konular:	Federated learning responsible AI edge computing machine learning data mining collaborative AI
Online Erişim:	https://ieeexplore.ieee.org/document/11153937/
Etiketler:	Etiketle Etiket eklenmemiş, İlk siz ekleyin!

Optimizing Fault Tolerance and Latency of Federated Learning Using Edge Servers and Pre-Trained Model

Benzer Materyaller