FeDiSa: A Semi-asynchronous Federated Learning Framework for Power
System Fault and Cyberattack Discrimination
- URL: http://arxiv.org/abs/2303.16956v1
- Date: Tue, 28 Mar 2023 13:34:38 GMT
- Title: FeDiSa: A Semi-asynchronous Federated Learning Framework for Power
System Fault and Cyberattack Discrimination
- Authors: Muhammad Akbar Husnoo, Adnan Anwar, Haftu Tasew Reda, Nasser
Hosseizadeh, Shama Naz Islam, Abdun Naser Mahmood, Robin Doss
- Abstract summary: This paper proposes FeDiSa, a novel Semi-asynchronous Federated learning framework for power system faults and cyberattack Discrimination.
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.
- Score: 1.0621485365427565
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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