ASR-Fed: agnostic straggler-resilient semi-asynchronous federated learning technique for secured drone network

Federated Learning (FL) has emerged as a transformative artificial intelligence paradigm, facilitating knowledge sharing among distributed edge devices while upholding data privacy. However, dynamic networks and resource-constrained devices such as drones, face challenges like power outages and netw...

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Bibliographic Details
Published in:International journal of machine learning and cybernetics Vol. 15; no. 11; pp. 5303 - 5319
Main Authors: Ihekoronye, Vivian Ukamaka, Nwakanma, Cosmas Ifeanyi, Kim, Dong-Seong, Lee, Jae Min
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2024
Springer Nature B.V
Subjects:
Accuracy
Algorithms
Artificial Intelligence
Clients
Communication
Complex Systems
Computational Intelligence
Control
Cybersecurity
Deep learning
Design
Drones
Effectiveness
Efficiency
Engineering
Federated learning
Flexibility
Learning
Machine learning
Mechatronics
Optimization techniques
Original Article
Pattern Recognition
Privacy
Robotics
Systems Biology
Wireless networks
Semi-asynchronous technique
Cybersecurity
Federated learning
Straggler effect
Intrusion detection
Drone security networks
ISSN:1868-8071, 1868-808X
Online Access:Get full text
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Summary:Federated Learning (FL) has emerged as a transformative artificial intelligence paradigm, facilitating knowledge sharing among distributed edge devices while upholding data privacy. However, dynamic networks and resource-constrained devices such as drones, face challenges like power outages and network contingencies, leading to the straggler effect that impedes the global model performance. To address this, we present ASR-Fed, a novel agnostic straggler-resilient semi-asynchronous FL aggregating algorithm. ASR-Fed incorporates a selection function to dynamically utilize updates from high-performing and active clients, while circumventing contributions from straggling clients during future aggregations. We evaluate the effectiveness of ASR-Fed using two prominent cyber-security datasets, WSN-DS, and Edge-IIoTset, and perform simulations with different deep learning models across formulated unreliable network scenarios. The simulation results demonstrate ASR-Fed’s effectiveness in achieving optimal accuracy while significantly reducing communication costs when compared with other FL aggregating protocols.
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ISSN:1868-8071
1868-808X
DOI:10.1007/s13042-024-02238-9