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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Vydané v:	Neural computing & applications Ročník 37; číslo 26; s. 21563 - 21605
Hlavní autori:	Ghosh, Subhas Kumar, Vittamsetti, Vijay Monic
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Springer London 01.09.2025 Springer Nature B.V
Predmet:	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
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