Federated Learning for Decentralized Electricity Market Optimization: A Review and Research Agenda

Tymoteusz Miller (), Irmina Durlik, Ewelina Kostecka, Polina Kozlovska and Aleksander Nowak
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Tymoteusz Miller: Institute of Marine and Environmental Sciences, University of Szczecin, Wąska 13, 71-415 Szczecin, Poland
Irmina Durlik: Faculty of Navigation, Maritime University of Szczecin, Waly Chrobrego 1-2, 70-500 Szczecin, Poland
Ewelina Kostecka: Faculty of Mechatronics and Electrical Engineering, Maritime University of Szczecin, Waly Chrobrego 1-2, 70-500 Szczecin, Poland
Polina Kozlovska: Faculty of Economics, Finance and Management, University of Szczecin, 71-415 Szczecin, Poland
Aleksander Nowak: Faculty of Mechatronics and Electrical Engineering, Maritime University of Szczecin, Waly Chrobrego 1-2, 70-500 Szczecin, Poland

Energies, 2025, vol. 18, issue 17, 1-27

Abstract: Decentralized electricity markets are increasingly shaped by the proliferation of distributed energy resources, the rise of prosumers, and growing demands for privacy-aware analytics. In this context, federated learning (FL) emerges as a promising paradigm that enables collaborative model training without centralized data aggregation. This review systematically explores the application of FL in energy systems, with particular attention to architectures, heterogeneity management, optimization tasks, and real-world use cases such as load forecasting, market bidding, congestion control, and predictive maintenance. The article critically examines evaluation practices, reproducibility issues, regulatory ambiguities, ethical implications, and interoperability barriers. It highlights the limitations of current benchmarking approaches and calls for domain-specific FL simulation environments. By mapping the intersection of technical design, market dynamics, and institutional constraints, the article formulates a pluralistic research agenda for scalable, fair, and secure FL deployments in modern electricity systems. This work positions FL not merely as a technical innovation but as a socio-technical intervention, requiring co-design across engineering, policy, and human factors.

Keywords: federated learning; decentralized electricity markets; smart grid; load forecasting; bidding strategies (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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