ADAPTIVE MULTILAYER ARCHITECTURES FOR INTELLIGENT EDGE COMPUTING LEVERAGING FEDERATED LEARNING AND ENERGY-EFFICIENT NEURAL OPTIMIZATION IN DISTRIBUTED ELECTRICAL SYSTEMS
Keywords:
Edge computing, federated learning, neural optimization, energy efficiency, distributed systems, IoT, smart grid, adaptive architectureAbstract
As the demand for real-time data processing and decentralized intelligence grows, intelligent edge computing has emerged as a critical paradigm. This paper investigates adaptive multilayer architectures for edge computing that integrate federated learning (FL) with energy-efficient neural optimization strategies to address the challenges of scalability, latency, and power constraints in distributed electrical systems. We present a structured analysis of the interplay between edge intelligence, neuromorphic models, and distributed infrastructure, exploring architectural patterns that enable seamless learning across heterogeneous IoT nodes. Furthermore, we analyze key literature contributions, examine architectural designs, and evaluate future directions with a focus on sustainability, resilience, and autonomous operation.
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