# A Federated Learning Framework for Smart Grids: Securing Power Traces in Collaborative Learning

arXiv: https://arxiv.org/abs/2103.11870 · HTML: https://mlsanity.com/paper/2103.11870/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 22 Mar 2021.

## Builds on (most cited first, 7 of 7)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Robust and Communication-Efficient Federated Learning from Non-IID Data](https://mlsanity.com/paper/1903.02891/) (2019)
- [SecureBoost: A Lossless Federated Learning Framework](https://mlsanity.com/paper/1901.08755/) (2019)
- [Hierarchical Federated Learning Across Heterogeneous Cellular Networks](https://mlsanity.com/paper/1909.02362/) (2019)
- [Model-Free Renewable Scenario Generation Using Generative Adversarial Networks](https://mlsanity.com/paper/1707.09676/) (2017)
- [Energy Demand Prediction with Federated Learning for Electric Vehicle Networks](https://mlsanity.com/paper/1909.00907/) (2019)

## Built on by (most cited first, 11 of 11)

- [Vertical Federated Learning: Concepts, Advances and Challenges](https://mlsanity.com/paper/2211.12814/) (2022)
- [Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey](https://mlsanity.com/paper/2405.17495/) (2024)
- [Federated Learning with Hyperparameter-based Clustering for Electrical Load Forecasting](https://mlsanity.com/paper/2111.07462/) (2021)
- [Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives](https://mlsanity.com/paper/2304.08602/) (2023)
- [Federated Learning for Cyber Physical Systems: A Comprehensive Survey](https://mlsanity.com/paper/2505.04873/) (2025)
- [Federated learning and next generation wireless communications: A survey on bidirectional relationship](https://mlsanity.com/paper/2110.07649/) (2021)
- [A Fair and Efficient Hybrid Federated Learning Framework based on XGBoost for Distributed Power Prediction](https://mlsanity.com/paper/2201.02783/) (2022)
- [FedWOA: A Federated Learning Model that uses the Whale Optimization Algorithm for Renewable Energy Prediction](https://mlsanity.com/paper/2309.10337/) (2023)
- [Edge AI for Internet of Energy: Challenges and Perspectives](https://mlsanity.com/paper/2311.16851/) (2023)
- [Privacy-Preserving Collaborative Split Learning Framework for Smart Grid Load Forecasting](https://mlsanity.com/paper/2403.01438/) (2024)
- [A Bayesian Vertical Federated Learning Framework for Multivariate Reduced-Rank High-Dimensional Regression](https://mlsanity.com/paper/2609.22654/) (2026)

## Abstract (arXiv)

With the deployment of smart sensors and advancements in communication technologies, big data analytics have become vastly popular in the smart grid domain, informing stakeholders of the best power utilization strategy. However, these power-related data are stored and owned by different parties. For example, power consumption data are stored in numerous transformer stations across cities; mobility data of the population, which are important indicators of power consumption, are held by mobile companies. Direct data sharing might compromise party benefits, individual privacy and even national security. Inspired by the federated learning scheme from Google AI, we propose a federated learning framework for smart grids, which enables collaborative learning of power consumption patterns without leaking individual power traces. Horizontal federated learning is employed when data are scattered in the sample space; vertical federated learning, on the other hand, is designed for the case with data scattered in the feature space. Case studies show that, with proper encryption schemes such as Paillier encryption, the machine learning models constructed from the proposed framework are lossless, privacy-preserving and effective. Finally, the promising future of federated learning in other facets of the smart grid is discussed, including electric vehicles, distributed generation/consumption and integrated energy systems.