Communication-Aware Federated Learning for Energy Management in Edge-Cloud Autonomous Systems

Lightweight Aggregation Protocols for Green Federated Learning in Vehicle-to-Cloud Systems

Event-Triggered Decentralized Intelligence with Energy-Aware Federated Learning for Real-World Systems

The rapid growth of large-scale interconnected systems, such as smart cities, industrial automation, and environmental monitoring, demands intelligent decision-making frameworks that are resilient, scalable, and resource-efficient. Traditional centralized intelligence approaches suffer from communication bottlenecks, h...

Federated reinforcement learning for energy-aware load balancing in edge-fog-cloud IoT continuum

A federated reinforcement learning framework for adaptive load balancing in the edge-fog-cloud continuum that optimizes energy efficiency and supports diverse quality of service requirements and uses a distributed experience replay buffer to reduce trial-and-error in reinforcement learning.

Energy-Efficient Distributed Machine Learning in Edge Computing Architectures

This study investigates energy-efficient distributed machine learning techniques, including federated learning, model compression, adaptive resource management, dynamic task offloading, and communication-efficient optimization, and proposes a distributed learning framework that integrates local model training, adaptive...

Digital twin enabled federated reinforcement learning for energy efficient spectrum allocation in heterogeneous vehicular networks

Digital Twin-Based Low-Energy Reinforcement Learning for Multi-Cell IoV (DT-LERL) distributed collaborative training architecture in cellular-based IoV scenarios is designed, which allows the twin to replace the end-side vehicular entities by introducing digital twins to carry out scenario interactions and model traini...

Energy-Optimized Federated Aggregation for Predictive Networking in Vehicular Cloud Architectures

An Energy-Optimized Federated Aggregation architecture of Predictive Networking in Vehicular Cloud Architectures (EOFA-PNVC) integrating client selection, gradient compression, and an energy-aware weighting scheme with a forecasting head that handles short-horizon state prediction of networks is suggested.