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

IF 3.1 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Machine Learning and Cybernetics Pub Date : 2024-07-15 DOI:10.1007/s13042-024-02238-9

Vivian Ukamaka Ihekoronye, C. I. Nwakanma, Dong‐Seong Kim, Jae Min Lee

引用次数: 0

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ASR-Fed：面向安全无人机网络的不可知的抗流浪者半同步联合学习技术

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Machine Learning and Cybernetics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-

CiteScore

7.90

自引率

10.70%

发文量

225

期刊介绍： Cybernetics is concerned with describing complex interactions and interrelationships between systems which are omnipresent in our daily life. Machine Learning discovers fundamental functional relationships between variables and ensembles of variables in systems. The merging of the disciplines of Machine Learning and Cybernetics is aimed at the discovery of various forms of interaction between systems through diverse mechanisms of learning from data. The International Journal of Machine Learning and Cybernetics (IJMLC) focuses on the key research problems emerging at the junction of machine learning and cybernetics and serves as a broad forum for rapid dissemination of the latest advancements in the area. The emphasis of IJMLC is on the hybrid development of machine learning and cybernetics schemes inspired by different contributing disciplines such as engineering, mathematics, cognitive sciences, and applications. New ideas, design alternatives, implementations and case studies pertaining to all the aspects of machine learning and cybernetics fall within the scope of the IJMLC. Key research areas to be covered by the journal include: Machine Learning for modeling interactions between systems Pattern Recognition technology to support discovery of system-environment interaction Control of system-environment interactions Biochemical interaction in biological and biologically-inspired systems Learning for improvement of communication schemes between systems