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Generalized Policy Learning for Smart Grids: FL TRPO Approach

arXiv 2403.18439 ↗First posted 27 Mar 2024cs.LGcited by 0

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Abstract (arXiv)

The smart grid domain requires bolstering the capabilities of existing energy management systems; Federated Learning (FL) aligns with this goal as it demonstrates a remarkable ability to train models on heterogeneous datasets while maintaining data privacy, making it suitable for smart grid applications, which often involve disparate data distributions and interdependencies among features that hinder the suitability of linear models. This paper introduces a framework that combines FL with a Trust Region Policy Optimization (FL TRPO) aiming to reduce energy-associated emissions and costs. Our approach reveals latent interconnections and employs personalized encoding methods to capture unique insights, understanding the relationships between features and optimal strategies, allowing our model to generalize to previously unseen data. Experimental results validate the robustness of our approach, affirming its proficiency in effectively learning policy models for smart grid challenges.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 17 references that are in the map. First posted on arXiv 27 Mar 2024.

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References · 11

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2015Trust Region Policy Optimizationcited by 2,503
  3. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 451
  4. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 391
  5. 2020HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clientscited by 334
  6. 2019Tackling Climate Change with Machine Learningcited by 264
  7. 2019Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communicationcited by 218
  8. 2022SplitGP: Achieving Both Generalization and Personalization in Federated Learningcited by 20
  9. 2021Model-predictive control and reinforcement learning in multi-energy system case studiescited by 10
  10. 2022A Federated DRL Approach for Smart Micro-Grid Energy Control with Distributed Energy Resourcescited by 8
  11. 2023MAHTM: A Multi-Agent Framework for Hierarchical Transactive Microgridscited by 3

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