Distributed optimization with faulty nodes: robust aggregation in hyperbolic space

The increasing deployment of distributed machine learning models necessitates robust optimization methods that can tolerate adversarial or faulty nodes. In this work, we propose a robust gradient aggregation method for distributed stochastic gradient descent that leverages hyperbolic geometry. Speci...

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Bibliographic Details
Published in:	Neural computing & applications Vol. 37; no. 26; pp. 21563 - 21605
Main Authors:	Ghosh, Subhas Kumar, Vittamsetti, Vijay Monic
Format:	Journal Article
Language:	English
Published:	London Springer London 01.09.2025 Springer Nature B.V
Subjects:	Accuracy Artificial Intelligence Communication Computation Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Euclidean geometry Euclidean space Federated learning Hyperbolic coordinates Image Processing and Computer Vision Machine learning Network management systems Nodes Optimization Optimization algorithms Original Article Privacy Probability and Statistics in Computer Science Robustness Sensors Traffic flow Distributed optimization algorithm Federated learning Stochastic gradient descent Robust aggregation rule Byzantine resilience Hyperbolic space
ISSN:	0941-0643, 1433-3058
Online Access:	Get full text
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