scholarly journals IEEE TCCN Special Section Editorial: Convergence of Collaborative Distributed Machine Learning and Edge Computing Toward Intelligent Networking

2021 ◽  
Vol 7 (4) ◽  
pp. 1016-1020
Author(s):  
Dusit Niyato ◽  
Kaibin Huang ◽  
Mehdi Bennis ◽  
Miao Pan ◽  
Zehui Xiong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Special Section ◽  
Edge Computing ◽  
Intelligent Networking ◽  
Distributed Machine Learning

Distributed Machine Learning for Multiuser Mobile Edge Computing Systems

2022 ◽  
pp. 1-1
Author(s):  
Yinghao Guo ◽  
Rui Zhao ◽  
Shiwei Lai ◽  
Lisheng Fan ◽  
Xianfu Lei ◽  
...  
Keyword(s):  
Machine Learning ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Computing Systems ◽  
Distributed Machine Learning

scholarly journals Design of a dynamic and self-adapting system, supported with artificial intelligence, machine learning and real-time intelligence for predictive cyber risk analytics in extreme environments – cyber risk in the colonisation of Mars

2021 ◽  
Author(s):  
Petar Radanliev ◽  
David De Roure ◽  
Kevin Page ◽  
Max Van Kleek ◽  
Omar Santos ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Cyber Security ◽  
Extreme Environments ◽  
Learning Technologies ◽  
Cyber Attacks ◽  
Edge Computing ◽  
Mathematical Formulas ◽  
Cyber Risk

AbstractMultiple governmental agencies and private organisations have made commitments for the colonisation of Mars. Such colonisation requires complex systems and infrastructure that could be very costly to repair or replace in cases of cyber-attacks. This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing. The paper presents a new mathematical approach for integrating concepts for cognition engine design, edge computing and Artificial Intelligence and Machine Learning to automate anomaly detection. This engine instigates a step change by applying Artificial Intelligence and Machine Learning embedded at the edge of IoT networks, to deliver safe and functional real-time intelligence for predictive cyber risk analytics. This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when Artificial Intelligence and Machine Learning technologies are migrated to the periphery of the internet and into local IoT networks.

Joint Data Collection and Resource Allocation for Distributed Machine Learning at the Edge

2020 ◽  
pp. 1-1
Author(s):  
Min Chen ◽  
Haichuan Wang ◽  
Zeyu Meng ◽  
Hongli Xu ◽  
Yang Xu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Resource Allocation ◽  
Data Collection ◽  
Distributed Machine Learning

PODC 2020 Review

2021 ◽  
Vol 51 (4) ◽  
pp. 75-81
Author(s):  
Ahad Mirza Baig ◽  
Alkida Balliu ◽  
Peter Davies ◽  
Michal Dory
Keyword(s):  
Machine Learning ◽  
Distributed Computing ◽  
Keynote Speaker ◽  
Lively Discussion ◽  
Theoretical Understanding ◽  
New Directions ◽  
New Ideas ◽  
New Challenges ◽  
The Impact ◽  
Distributed Machine Learning

Rachid Guerraoui was the rst keynote speaker, and he got things o to a great start by discussing the broad relevance of the research done in our community relative to both industry and academia. He rst argued that, in some sense, the fact that distributed computing is so pervasive nowadays could end up sti ing progress in our community by inducing people to work on marginal problems, and becoming isolated. His rst suggestion was to try to understand and incorporate new ideas coming from applied elds into our research, and argued that this has been historically very successful. He illustrated this point via the distributed payment problem, which appears in the context of blockchains, in particular Bitcoin, but then turned out to be very theoretically interesting; furthermore, the theoretical understanding of the problem inspired new practical protocols. He then went further to discuss new directions in distributed computing, such as the COVID tracing problem, and new challenges in Byzantine-resilient distributed machine learning. Another source of innovation Rachid suggested was hardware innovations, which he illustrated with work studying the impact of RDMA-based primitives on fundamental problems in distributed computing. The talk concluded with a very lively discussion.

scholarly journals Editorial: Special Section on Pervasive Edge Computing for Industrial Internet of Things

2021 ◽  
Vol 17 (7) ◽  
pp. 5010-5011
Author(s):  
Zhaolong Ning ◽  
Edith Ngai ◽  
Ricky Y. K. Kwok ◽  
Mohammad S. Obaidat
Keyword(s):  
Internet Of Things ◽  
Special Section ◽  
Edge Computing ◽  
Industrial Internet Of Things ◽  
Industrial Internet
Sign in / Sign up

Export Citation Format

Share Document