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

Abstract

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.