# FeDiSa: A Semi-asynchronous Federated Learning Framework for Power System Fault and Cyberattack Discrimination

arXiv: https://arxiv.org/abs/2303.16956 · HTML: https://mlsanity.com/paper/2303.16956/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 20 references that are in the map.
- First posted on arXiv 28 Mar 2023.

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

- [Asynchronous Federated Optimization](https://mlsanity.com/paper/1903.03934/) (2019)
- [SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead](https://mlsanity.com/paper/1910.01355/) (2019)
- [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)
- [FedMood: Federated Learning on Mobile Health Data for Mood Detection](https://mlsanity.com/paper/2102.09342/) (2021)
- [A Secure Federated Learning Framework for Residential Short Term Load Forecasting](https://mlsanity.com/paper/2209.14547/) (2022)
- [False Data Injection Threats in Active Distribution Systems: A Comprehensive Survey](https://mlsanity.com/paper/2111.14251/) (2021)

## Built on by (most cited first, 1 of 1)

- [Decentralized Federated Anomaly Detection in Smart Grids: A P2P Gossip Approach](https://mlsanity.com/paper/2407.15879/) (2024)

## Abstract (arXiv)

With growing security and privacy concerns in the Smart Grid domain, intrusion detection on critical energy infrastructure has become a high priority in recent years. To remedy the challenges of privacy preservation and decentralized power zones with strategic data owners, Federated Learning (FL) has contemporarily surfaced as a viable privacy-preserving alternative which enables collaborative training of attack detection models without requiring the sharing of raw data. To address some of the technical challenges associated with conventional synchronous FL, this paper proposes FeDiSa, a novel Semi-asynchronous Federated learning framework for power system faults and cyberattack Discrimination which takes into account communication latency and stragglers. Specifically, we propose a collaborative training of deep auto-encoder by Supervisory Control and Data Acquisition sub-systems which upload their local model updates to a control centre, which then perform a semi-asynchronous model aggregation for a new global model parameters based on a buffer system and a preset cut-off time. Experiments on the proposed framework using publicly available industrial control systems datasets reveal superior attack detection accuracy whilst preserving data confidentiality and minimizing the adverse effects of communication latency and stragglers. Furthermore, we see a 35% improvement in training time, thus validating the robustness of our proposed method.