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Found 6 papers, 0 papers with code
Efficient Collaborations through Weight-Driven Coalition Dynamics in Federated Learning Systems
This is foundational for our system, directing the formation of coalitions among devices based on the closeness of their model weights.
Applications of machine learning and IoT for Outdoor Air Pollution Monitoring and Prediction: A Systematic Literature Review
The general objective of this paper is to systematically review applications of machine learning and Internet of Things (IoT) for outdoor air pollution prediction and the combination of monitoring sensors and input features used.
A Comparative Evaluation of FedAvg and Per-FedAvg Algorithms for Dirichlet Distributed Heterogeneous Data
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 preserving data privacy.
Vehicles Control: Collision Avoidance using Federated Deep Reinforcement Learning
Significantly, the FDDPG-based algorithm demonstrates substantial reductions in travel delays and notable improvements in average speed compared to the DDPG algorithm.
Federated Learning for Water Consumption Forecasting in Smart Cities
On the other hand, enormous data volumes with sufficient variation are needed for the deep learning models to be trained properly.
Experimental Investigation of Variational Mode Decomposition and Deep Learning for Short-Term Multi-horizon Residential Electric Load Forecasting
In this paper, we investigate the use of Variational Mode Decomposition and deep learning techniques to improve the accuracy of the load forecasting problem.
