# Decentralized Federated Anomaly Detection in Smart Grids: A P2P Gossip Approach

arXiv: https://arxiv.org/abs/2407.15879 · HTML: https://mlsanity.com/paper/2407.15879/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 20 Jul 2024.

## Builds on (most cited first, 8 of 8)

- [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)
- [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 Secure Federated Learning Framework for Residential Short Term Load Forecasting](https://mlsanity.com/paper/2209.14547/) (2022)
- [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)

## Abstract (arXiv)

The increasing security and privacy concerns in the Smart Grid sector have led to a significant demand for robust intrusion detection systems within critical smart grid infrastructure. To address the challenges posed by privacy preservation and decentralized power system zones with distinct data ownership, Federated Learning (FL) has emerged as a promising privacy-preserving solution which facilitates collaborative training of attack detection models without necessitating the sharing of raw data. However, FL presents several implementation limitations in the power system domain due to its heavy reliance on a centralized aggregator and the risks of privacy leakage during model update transmission. To overcome these technical bottlenecks, this paper introduces a novel decentralized federated anomaly detection scheme based on two main gossip protocols namely Random Walk and Epidemic. Our findings indicate that the Random Walk protocol exhibits superior performance compared to the Epidemic protocol, highlighting its efficacy in decentralized federated learning environments. Experimental validation of the proposed framework utilizing publicly available industrial control systems datasets demonstrates superior attack detection accuracy while safeguarding data confidentiality and mitigating the impact of communication latency and stragglers. Furthermore, our approach yields a notable 35% improvement in training time compared to conventional FL, underscoring the efficacy and robustness of our decentralized learning method.