Decentralized Federated Learning: A Survey on Security and Privacy

no code implementations • 25 Jan 2024 • Ehsan Hallaji, Roozbeh Razavi-Far, Mehrdad Saif, Boyu Wang, Qiang Yang

Federated learning has been rapidly evolving and gaining popularity in recent years due to its privacy-preserving features, among other advantages.

Federated Learning Privacy Preserving

Learning From High-Dimensional Cyber-Physical Data Streams for Diagnosing Faults in Smart Grids

no code implementations • 15 Mar 2023 • Hossein Hassani, Ehsan Hallaji, Roozbeh Razavi-Far, Mehrdad Saif

The performance of fault diagnosis systems is highly affected by data quality in cyber-physical power systems.

Dimensionality Reduction Feature Engineering +1

A Stream Learning Approach for Real-Time Identification of False Data Injection Attacks in Cyber-Physical Power Systems

no code implementations • 13 Oct 2022 • Ehsan Hallaji, Roozbeh Razavi-Far, Meng Wang, Mehrdad Saif, Bruce Fardanesh

Using this information, the signal retrieval module can easily recover the original control signal and remove the injected false data.

Retrieval

Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms

no code implementations • 5 Jul 2022 • Ehsan Hallaji, Roozbeh Razavi-Far, Mehrdad Saif

The advent of federated learning has facilitated large-scale data exchange amongst machine learning models while maintaining privacy.

Federated Learning Transfer Learning