# Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives

arXiv: https://arxiv.org/abs/2304.08602 · HTML: https://mlsanity.com/paper/2304.08602/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 159 references that are in the map.
- First posted on arXiv 17 Apr 2023.

## Builds on (most cited first, 30 of 31)

- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [Federated Learning for Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2104.07914/) (2021)
- [Model Pruning Enables Efficient Federated Learning on Edge Devices](https://mlsanity.com/paper/1909.12326/) (2019)
- [Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges](https://mlsanity.com/paper/2009.13012/) (2020)
- [Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding](https://mlsanity.com/paper/1708.05045/) (2017)
- [Federated Learning: Opportunities and Challenges](https://mlsanity.com/paper/2101.05428/) (2021)
- [Neural NILM: Deep Neural Networks Applied to Energy Disaggregation](https://mlsanity.com/paper/1507.06594/) (2015)
- [Energy Demand Prediction with Federated Learning for Electric Vehicle Networks](https://mlsanity.com/paper/1909.00907/) (2019)
- [Electrical Load Forecasting Using Edge Computing and Federated Learning](https://mlsanity.com/paper/2201.11248/) (2022)
- [Achieving Security and Privacy in Federated Learning Systems: Survey, Research Challenges and Future Directions](https://mlsanity.com/paper/2012.06810/) (2020)
- [Privacy-preserving Spatiotemporal Scenario Generation of Renewable Energies: A Federated Deep Generative Learning Approach](https://mlsanity.com/paper/2107.07738/) (2021)
- [A Federated Learning Approach to Anomaly Detection in Smart Buildings](https://mlsanity.com/paper/2010.10293/) (2020)
- [FedREP: Towards Horizontal Federated Load Forecasting for Retail Energy Providers](https://mlsanity.com/paper/2203.00219/) (2022)
- [Federated Learning for Short-term Residential Load Forecasting](https://mlsanity.com/paper/2105.13325/) (2021)
- [Federated Learning with Hyperparameter-based Clustering for Electrical Load Forecasting](https://mlsanity.com/paper/2111.07462/) (2021)
- [A Federated Learning Framework for Smart Grids: Securing Power Traces in Collaborative Learning](https://mlsanity.com/paper/2103.11870/) (2021)
- [FedNILM: Applying Federated Learning to NILM Applications at the Edge](https://mlsanity.com/paper/2106.07751/) (2021)
- [DER Forecast using Privacy Preserving Federated Learning](https://mlsanity.com/paper/2107.03248/) (2021)
- [The emergence of Explainability of Intelligent Systems: Delivering Explainable and Personalised Recommendations for Energy Efficiency](https://mlsanity.com/paper/2010.04990/) (2020)
- [Asynchronous Decentralized Federated Learning for Collaborative Fault Diagnosis of PV Stations](https://mlsanity.com/paper/2202.13606/) (2022)
- [A Review of Federated Learning in Energy Systems](https://mlsanity.com/paper/2208.10941/) (2022)
- [Federated Learning based Energy Demand Prediction with Clustered Aggregation](https://mlsanity.com/paper/2210.15850/) (2022)
- [Intelligent edge-based recommender system for internet of energy applications](https://mlsanity.com/paper/2111.13272/) (2021)
- [Empowering Prosumer Communities in Smart Grid with Wireless Communications and Federated Edge Learning](https://mlsanity.com/paper/2104.03169/) (2021)
- [Fed-BEV: A Federated Learning Framework for Modelling Energy Consumption of Battery Electric Vehicles](https://mlsanity.com/paper/2108.04036/) (2021)
- [Augmenting an Assisted Living Lab with Non-Intrusive Load Monitoring](https://mlsanity.com/paper/2002.05593/) (2020)
- [Smart Microgrids: Overview and Outlook](https://mlsanity.com/paper/1304.3944/) (2013)
- [Connecting Distributed Pockets of EnergyFlexibility through Federated Computations:Limitations and Possibilities](https://mlsanity.com/paper/2009.10182/) (2020)

## Built on by (most cited first, 6 of 6)

- [Federated Learning for Computer Vision](https://mlsanity.com/paper/2308.13558/) (2023)
- [SyncFed: Time-Aware Federated Learning through Explicit Timestamping and Synchronization](https://mlsanity.com/paper/2506.09660/) (2025)
- [Federated and Transfer Learning for Cancer Detection Based on Image Analysis](https://mlsanity.com/paper/2405.20126/) (2024)
- [Edge AI for Internet of Energy: Challenges and Perspectives](https://mlsanity.com/paper/2311.16851/) (2023)
- [Unveiling Hidden Energy Anomalies: Harnessing Deep Learning to Optimize Energy Management in Sports Facilities](https://mlsanity.com/paper/2402.08742/) (2024)
- [TS-Inverse: A Gradient Inversion Attack Tailored for Federated Time Series Forecasting Models](https://mlsanity.com/paper/2503.20952/) (2025)

## Abstract (arXiv)

Consumer's privacy is a main concern in Smart Grids (SGs) due to the sensitivity of energy data, particularly when used to train machine learning models for different services. These data-driven models often require huge amounts of data to achieve acceptable performance leading in most cases to risks of privacy leakage. By pushing the training to the edge, Federated Learning (FL) offers a good compromise between privacy preservation and the predictive performance of these models. The current paper presents an overview of FL applications in SGs while discussing their advantages and drawbacks, mainly in load forecasting, electric vehicles, fault diagnoses, load disaggregation and renewable energies. In addition, an analysis of main design trends and possible taxonomies is provided considering data partitioning, the communication topology, and security mechanisms. Towards the end, an overview of main challenges facing this technology and potential future directions is presented.