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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Summary: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. Specifically, local gradients computed at individual nodes are embedded into hyperbolic space using the Poincaré ball model, and their geometric median is computed as a robust aggregate. This aggregated gradient is then mapped back to Euclidean space for the gradient update. We also show that existing robust gradient aggregation methods like Krum can be improved using hyperbolic space. Compared to existing robust aggregation methods, our hyperbolic approach offers improved separation of outlier updates. We provide theoretical convergence guarantees and validate our method on benchmark datasets as well as on a traffic forecasting task, demonstrating its efficacy in mitigating Byzantine failures in distributed federated learning environments.
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-025-11475-0