Federated Learning for Privacy-Preserving Energy Management in Distributed Power Systems
Abstract
The Internet of Things has introduced the concept of Decentralized Energy Markets which enables peer-to-peer trading. This technology, together with federated learning and blockchain technology, is transforming the integration of renewable energy into smart grids and microgrids by improving efficiency, security, and consumer autonomy. This article presents a systematic review of the literature that synthesizes recent advances in decentralized energy market frameworks, transactive energy systems, and blockchain-based solutions for secure, and transparent energy trading. The findings have revealed that high computational costs, total reliance on simplified models, insufficient cybersecurity analysis, and lack of real-world validation persist limits the real applicability of these technologies in real-world applications. The article has proposed some potential solutions such as lightweight blockchain protocols, adaptive optimization algorithms, and large-scale field trials to ensure scalability, sustainability, and resilience. This review underscores the potential of decentralized energy markets to revolutionize the energy systems, particularly in a decentralized energy grid.
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