FedDiSC: A Computation-efficient Federated Learning Framework for Power Systems Disturbance and Cyber Attack Discrimination

no code implementations • 7 Apr 2023 • Muhammad Akbar Husnoo, Adnan Anwar, Haftu Tasew Reda, Nasser Hosseinzadeh, Shama Naz Islam, Abdun Naser Mahmood, Robin Doss

Lastly, to adapt our proposed framework to the timeliness of real-world cyberattack detection in SGs, we leverage the use of a gradient privacy-preserving quantization scheme known as DP-SIGNSGD to improve its communication efficiency.

Federated Learning Privacy Preserving +2

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

no code implementations • 28 Mar 2023 • Muhammad Akbar Husnoo, Adnan Anwar, Haftu Tasew Reda, Nasser Hosseizadeh, Shama Naz Islam, Abdun Naser Mahmood, Robin Doss

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.

Federated Learning Intrusion Detection +1