# Detection of False Data Injection Attacks in Smart Grid: A Secure Federated Deep Learning Approach

arXiv: https://arxiv.org/abs/2209.00778 · HTML: https://mlsanity.com/paper/2209.00778/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2022.

## Built on by (most cited first, 27 of 27)

- [On the Convergence of Decentralized Federated Learning Under Imperfect Information Sharing](https://mlsanity.com/paper/2303.10695/) (2023)
- [Wind Power Forecasting Considering Data Privacy Protection: A Federated Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2211.02674/) (2022)
- [Optimal scheduling of island integrated energy systems considering multi-uncertainties and hydrothermal simultaneous transmission: A deep reinforcement learning approach](https://mlsanity.com/paper/2212.13472/) (2022)
- [Localization of Dummy Data Injection Attacks in Power Systems Considering Incomplete Topological Information: A Spatio-Temporal Graph Wavelet Convolutional Neural Network Approach](https://mlsanity.com/paper/2401.15321/) (2024)
- [A review of federated learning in renewable energy applications: Potential, challenges, and future directions](https://mlsanity.com/paper/2312.11220/) (2023)
- [Multi-Microgrid Collaborative Optimization Scheduling Using an Improved Multi-Agent Soft Actor-Critic Algorithm](https://mlsanity.com/paper/2304.01223/) (2023)
- [Federated Learning Based Distributed Localization of False Data Injection Attacks on Smart Grids](https://mlsanity.com/paper/2306.10420/) (2023)
- [The Role of Deep Learning in Advancing Proactive Cybersecurity Measures for Smart Grid Networks: A Survey](https://mlsanity.com/paper/2401.05896/) (2024)
- [usfAD Based Effective Unknown Attack Detection Focused IDS Framework](https://mlsanity.com/paper/2403.11180/) (2024)
- [FedNMUT -- Federated Noisy Model Update Tracking Convergence Analysis](https://mlsanity.com/paper/2403.13247/) (2024)
- [FeDiSa: A Semi-asynchronous Federated Learning Framework for Power System Fault and Cyberattack Discrimination](https://mlsanity.com/paper/2303.16956/) (2023)
- [FedDiSC: A Computation-efficient Federated Learning Framework for Power Systems Disturbance and Cyber Attack Discrimination](https://mlsanity.com/paper/2304.03640/) (2023)
- [Mitigating Data Injection Attacks on Federated Learning](https://mlsanity.com/paper/2312.02102/) (2023)
- [RepuNet: A Reputation System for Mitigating Malicious Clients in DFL](https://mlsanity.com/paper/2506.19892/) (2025)
- [Neural Network-Based Detection and Multi-Class Classification of FDI Attacks in Smart Grid Home Energy Systems](https://mlsanity.com/paper/2508.10035/) (2025)
- [ZTFed-MAS2S: A Zero-Trust Federated Learning Framework with Verifiable Privacy and Trust-Aware Aggregation for Wind Power Data Imputation](https://mlsanity.com/paper/2508.18318/) (2025)
- [Screen Before You Fetch: Compressed Byzantine Screening for Decentralized Learning on the Edge-Cloud Continuum](https://mlsanity.com/paper/2510.07922/) (2025)
- [The State of the Art in transformer fault diagnosis with artificial intelligence and Dissolved Gas Analysis: A Review of the Literature](https://mlsanity.com/paper/2304.11880/) (2023)
- [Federated Learning Forecasting for Strengthening Grid Reliability and Enabling Markets for Resilience](https://mlsanity.com/paper/2407.11571/) (2024)
- [Decentralized Federated Anomaly Detection in Smart Grids: A P2P Gossip Approach](https://mlsanity.com/paper/2407.15879/) (2024)
- [Cybersecurity Assessment of Smart Grid Exposure Using a Machine Learning Based Approach](https://mlsanity.com/paper/2501.14175/) (2025)
- [Residual-Evasive Attacks on ADMM in Distributed Optimization](https://mlsanity.com/paper/2504.18570/) (2025)
- [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)
- [QSTAformer: A Quantum-Enhanced Transformer for Robust Short-Term Voltage Stability Assessment against Adversarial Attacks](https://mlsanity.com/paper/2512.09936/) (2025)
- [Quantum Machine Learning Approaches for Coordinated Stealth Attack Detection in Distributed Generation Systems](https://mlsanity.com/paper/2601.00873/) (2025)
- [Cyber-Physical Anomaly Detection in IoT-Enabled Smart Grids Using Machine Learning and Metaheuristic Feature Optimization](https://mlsanity.com/paper/2605.22749/) (2026)

## Abstract (arXiv)

As an important cyber-physical system (CPS), smart grid is highly vulnerable to cyber attacks. Amongst various types of attacks, false data injection attack (FDIA) proves to be one of the top-priority cyber-related issues and has received increasing attention in recent years. However, so far little attention has been paid to privacy preservation issues in the detection of FDIAs in smart grid. Inspired by federated learning, a FDIA detection method based on secure federated deep learning is proposed in this paper by combining Transformer, federated learning and Paillier cryptosystem. The Transformer, as a detector deployed in edge nodes, delves deep into the connection between individual electrical quantities by using its multi-head self-attention mechanism. By using federated learning framework, our approach utilizes the data from all nodes to collaboratively train a detection model while preserving data privacy by keeping the data locally during training. To improve the security of federated learning, a secure federated learning scheme is designed by combing Paillier cryptosystem with federated learning. Through extensive experiments on the IEEE 14-bus and 118-bus test systems, the effectiveness and superiority of the proposed method are verifed.