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, high energy demands, and vulnerability to single points of failure, making them unsuitable for realworld deployment. This work introduces an event-triggered decentralized intelligence framework with energy-aware federated learning designed to address these challenges. In the proposed system, distributed nodes collaborate by exchanging model updates only when significant events or anomalies occur, rather than relying on continuous communication. This event-driven strategy substantially reduces bandwidth consumption while enabling timely adaptation to dynamic environments. To further enhance sustainability, the framework integrates energy-aware scheduling, allowing devices with limited power resources to contribute adaptively based on their energy profiles. A multilayer coordination mechanism ensures local autonomy and global consensus without centralized control. Experimental evaluations on representative real-world datasets demonstrate that the proposed method achieves competitive accuracy compared to conventional federated learning while reducing communication overhead by more than 40% and extending device lifetime in energy-constrained settings. Additionally, the framework incorporates Byzantine-resilient aggregation and is analyzed under communication latency and varying network topology conditions.

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 communication scheduling, gradient compression, and workload balancing to minimize energy consumption while maintaining learning accuracy.

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 training.

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.