首页|New Findings from University of Batna in the Area of Machine Learning Reported ( Byzantine Fault Tolerance In Distributed Machine Learning: a Survey)
New Findings from University of Batna in the Area of Machine Learning Reported ( Byzantine Fault Tolerance In Distributed Machine Learning: a Survey)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Machine Learn ing have been published. According to news reporting originating from Batna, Alg eria, by NewsRx correspondents, research stated, "Byzantine Fault Tolerance (BFT ) is crucial for ensuring the resilience of Distributed Machine Learning (DML) s ystems during training under adversarial conditions. Among the rising corpus of research on BFT in DML, there is no comprehensive classification of techniques o r broad analysis of different approaches." Our news editors obtained a quote from the research from the University of Batna,"This paper provides an in-depth survey of recent advancements in BFT for DML, with a focus on first-order optimisation methods, particularly, the popular one Stochastic Gradient Descent (SGD) during the training phase. We offer a novel c lassification of BFT approaches based on characteristics such as the communicati on process, optimisation method, and topology setting. This classification aims to enhance the understanding of various BFT methods and guide future research in addressing open challenges in the field."
BatnaAlgeriaCyborgsEmerging Techno logiesMachine LearningUniversity of Batna
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