85 citations · 269 across the 23 of their papers we have counts for
8 papers · 1 filter
Semi-decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approach
Mahmoud Nazzal, Abdallah Khreishah, Joyoung Lee +3
Prediction of taxi service demand and supply is essential for improving customer's experience and provider's profit. Recently, graph neural networks (GNNs) have been shown promisin…
Warmup and Transfer Knowledge-Based Federated Learning Approach for IoT Continuous Authentication
Mohamad Wazzeh, Hakima Ould-Slimane, Chamseddine Talhi +2
Continuous behavioural authentication methods add a unique layer of security by allowing individuals to verify their unique identity when accessing a device. Maintaining session au…
FedMint: Intelligent Bilateral Client Selection in Federated Learning with Newcomer IoT Devices
Osama Wehbi, Sarhad Arisdakessian, Omar Abdel Wahab +4
Federated Learning (FL) is a novel distributed privacy-preserving learning paradigm, which enables the collaboration among several participants (e.g., Internet of Things devices) f…
Energy Pricing in P2P Energy Systems Using Reinforcement Learning
Nicolas Avila, Shahad Hardan, Elnura Zhalieva +2
The increase in renewable energy on the consumer side gives place to new dynamics in the energy grids. Participants in a microgrid can produce energy and trade it with their peers…
A Practical Cross-Device Federated Learning Framework over 5G Networks
Wenti Yang, Naiyu Wang, Zhitao Guan +3
The concept of federated learning (FL) was first proposed by Google in 2016. Thereafter, FL has been widely studied for the feasibility of application in various fields due to its…
PerFED-GAN: Personalized Federated Learning via Generative Adversarial Networks
Xingjian Cao, Gang Sun, Hongfang Yu +1
Federated learning is gaining popularity as a distributed machine learning method that can be used to deploy AI-dependent IoT applications while protecting client data privacy and…