38 citations · 72 across the 2 of their papers we have counts for
3 papers
cs.DC2022★ 34 cited
Data-Quality Based Scheduling for Federated Edge Learning
Afaf Taik, Hajar Moudoud, Soumaya Cherkaoui
FEderated Edge Learning (FEEL) has emerged as a leading technique for privacy-preserving distributed training in wireless edge networks, where edge devices collaboratively train ma…
eess.SP2021★ 38 cited
Empowering Prosumer Communities in Smart Grid with Wireless Communications and Federated Edge Learning
Afaf Taik, Boubakr Nour, Soumaya Cherkaoui
The exponential growth of distributed energy resources is enabling the transformation of traditional consumers in the smart grid into prosumers. Such transition presents a promisin…
cs.DC2021
Data-Aware Device Scheduling for Federated Edge Learning
Afaf Taik, Zoubeir Mlika, Soumaya Cherkaoui
Federated Edge Learning (FEEL) involves the collaborative training of machine learning models among edge devices, with the orchestration of a server in a wireless edge network. Due…