most citedFederated Learning for Water Consumption Forecasting in Smart Cities

4 citations · 7 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2024

Efficient Collaborations through Weight-Driven Coalition Dynamics in Federated Learning Systems

Mohammed El Hanjri, Hamza Reguieg, Adil Attiaoui +3

In the era of the Internet of Things (IoT), decentralized paradigms for machine learning are gaining prominence. In this paper, we introduce a federated learning model that capital…

cs.LG20241 cited

Applications of machine learning and IoT for Outdoor Air Pollution Monitoring and Prediction: A Systematic Literature Review

Ihsane Gryech, Chaimae Assad, Mounir Ghogho +1

According to the World Health Organization (WHO), air pollution kills seven million people every year. Outdoor air pollution is a major environmental health problem affecting low,…

cs.LG2023

A Comparative Evaluation of FedAvg and Per-FedAvg Algorithms for Dirichlet Distributed Heterogeneous Data

Hamza Reguieg, Mohammed El Hanjri, Mohamed El Kamili +1

In this paper, we investigate Federated Learning (FL), a paradigm of machine learning that allows for decentralized model training on devices without sharing raw data, there by pre…

cs.LG20232 cited

Vehicles Control: Collision Avoidance using Federated Deep Reinforcement Learning

Badr Ben Elallid, Amine Abouaomar, Nabil Benamar +1

In the face of growing urban populations and the escalating number of vehicles on the roads, managing transportation efficiently and ensuring safety have become critical challenges…

cs.LG20234 cited

Federated Learning for Water Consumption Forecasting in Smart Cities

Mohammed El Hanjri, Hibatallah Kabbaj, Abdellatif Kobbane +1

Water consumption remains a major concern among the world's future challenges. For applications like load monitoring and demand response, deep learning models are trained using eno…