# Federated Learning Based Distributed Localization of False Data Injection Attacks on Smart Grids

arXiv: https://arxiv.org/abs/2306.10420 · HTML: https://mlsanity.com/paper/2306.10420/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 38 references that are in the map.
- First posted on arXiv 17 Jun 2023.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://mlsanity.com/paper/1907.09693/) (2019)
- [Energy Demand Prediction with Federated Learning for Electric Vehicle Networks](https://mlsanity.com/paper/1909.00907/) (2019)
- [FedGraph: Federated Graph Learning with Intelligent Sampling](https://mlsanity.com/paper/2111.01370/) (2021)
- [Electrical Load Forecasting Using Edge Computing and Federated Learning](https://mlsanity.com/paper/2201.11248/) (2022)
- [Detection of False Data Injection Attacks in Smart Grid: A Secure Federated Deep Learning Approach](https://mlsanity.com/paper/2209.00778/) (2022)
- [Graph Neural Networks Based Detection of Stealth False Data Injection Attacks in Smart Grids](https://mlsanity.com/paper/2104.02012/) (2021)
- [Machine Learning Methods for Attack Detection in the Smart Grid](https://mlsanity.com/paper/1503.06468/) (2015)
- [Joint Detection and Localization of Stealth False Data Injection Attacks in Smart Grids using Graph Neural Networks](https://mlsanity.com/paper/2104.11846/) (2021)
- [Detection of false data injection attacks in smart grids based on graph signal processing](https://mlsanity.com/paper/1810.04894/) (2018)
- [Detection and Locating Cyber and Physical Stresses in Smart Grids using Graph Signal Processing](https://mlsanity.com/paper/2006.06095/) (2020)

## Built on by (most cited first, 2 of 2)

- [Clustered Federated Learning for Generalizable FDIA Detection in Smart Grids with Heterogeneous Data](https://mlsanity.com/paper/2507.14999/) (2025)
- [Federated Spatiotemporal Graph Learning for Passive Attack Detection in Smart Grids](https://mlsanity.com/paper/2510.02371/) (2025)

## Abstract (arXiv)

Data analysis and monitoring on smart grids are jeopardized by attacks on cyber-physical systems. False data injection attack (FDIA) is one of the classes of those attacks that target the smart measurement devices by injecting malicious data. The employment of machine learning techniques in the detection and localization of FDIA is proven to provide effective results. Training of such models requires centralized processing of sensitive user data that may not be plausible in a practical scenario. By employing federated learning for the detection of FDIA attacks, it is possible to train a model for the detection and localization of the attacks while preserving the privacy of sensitive user data. However, federated learning introduces new problems such as the personalization of the detectors in each node. In this paper, we propose a federated learning-based scheme combined with a hybrid deep neural network architecture that exploits the local correlations between the connected power buses by employing graph neural networks as well as the temporal patterns in the data by using LSTM layers. The proposed mechanism offers flexible and efficient training of an FDIA detector in a distributed setup while preserving the privacy of the clients. We validate the proposed architecture by extensive simulations on the IEEE 57, 118, and 300 bus systems and real electricity load data.