Machine Learning for Dependable Edge Computing Systems and Services

Dear Colleagues, 

Recent advances in machine learning (ML) techniques, particularly deep learning (DL), reinforcement learning, and federated learning, have successfully caused a huge number of breakthroughs in various application domains. Internet of Things (IoT) systems and applications consist of ubiquitously interconnecting devices (e.g., wireless sensors, wearable/mobile devices, cameras, smart tags, robots/UAVs, etc.). The urgent requirement of responsiveness and privacy led to Edge computing, a new paradigm that pushes the power of data analytics and computing capability to the edge of a network, closer to where the data are generated.  Huge challenges exist in the design, implementation, deployment and maintenance of trustworthy and reliable Edge systems’ infrastructures, algorithms, and applications.  ML and DL technologies are well-suited and insightful for use in the provision automated data and resource management and offer advanced secure and robust malicious behaviour detection, thereby significantly improving the trusted intelligence and operational efficiency. 

This Special Issue will cover, but not be limited to, the following topics:

• ML for dependable middleware and infrastructure design;
• ML for end-to-end performance tracing, debugging, and prediction;
• ML for security and privacy in Edge computing, including anomaly detection, anomaly diagnosis, intrusion/malware/fraud detection, cyber-attack detection, lightweight access control framework, etc.;
• ML-assisted interoperability and collaboration between edge devices and clouds;
• Interpretability and robustness of ML for edge computing systems;
• ML for autonomous systems and applications;
• ML-assisted fault tolerance in Edge computing infrastructures and applications;
• Privacy preserving federated learning for Edge computing infrastructures and applications;
• Reinforcement learning for edge computing systems, particularly in resource management and Quality of Service;
• Risk and threat detection and analysis for edge applications;
• Open datasets of edge computing systems for system and ML research;
• Big data analytics frameworks for edge computing;
• Distributed training and neural architecture search for/on the edge.

Dr. Renyu Yang 
Prof. Dr. Zhenyu Wen 
Dr. Xu Wang 
Dr. Prosanta Gope
Dr. Bin Shi 
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Big Data and Cognitive Computing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• machine learning
• deep learning
• edge computing
• dependability
• security and privacy
• fault-tolerance
• anomaly detection
• anomaly diagnosis
• access control
• federated learning
• reinforcement learning