Advances in Parallel and Distributed AI Computing

Dear Colleagues,

Distributed and parallel systems have now been investigated for more than six decades. Furthermore, parallel systems are now widely used, but are often prone to limited scalability. Distributed computing is a key technology for scalable software systems, especially in the context of data science and sensor networks. In contrast to a centralized AI infrastructure, where all data and calculations take place in a single place, the distribution of AI over several nodes allows the processing of a large amount of data, enabling complex task solving to be carried out. Moreover, computing power can be scaled to train more sophisticated AI models. With respect to sensor networks—including the IoT and edge computing networks—the distribution enables, on the one hand, the local processing and a reduction in the amount data, and, on the other hand, more resilient and robust networks. Furthermore, distributed computing is inherently coupled with self-* concepts—self-organization, self-adaptivity, and self-healing, amongst others. Of particular interest for this Special Issue are new architectures, services (AIaaS and MLaaS) and virtualization, as well as field studies demonstrating the deployment of distributed architectures in AI/ML as well as sensing systems. The main objective of this Special Issue is to attract high-quality research that presents emerging solutions, enabling technologies, and/or applications based on efficient and reliable distributed and parallel AI techniques, as, for example, with multi-agent systems coupled with machine learning that address recent challenges in intelligent distributed systems to improve robustness, adaptivity, and scalability.

Topics of interest include but are not limited to the following:

• Application of artificial intelligence in distributed systems;
• Application of machine learning algorithms in distributed systems, in particular very small low-resource devices deployed in IoT/edge environments;
• Distributed algorithms, workload balancing, communication and coordination, and sensor fusion—from theory to practice;
• Distributed data storage;
• Management solutions for large volumes of data (big data);
• Distributed and parallel architectures for AI/ML applications;
• Virtualization and distributed virtual machines;
• Multi-agent systems;
• High-performance and efficient distributed systems;
• New computer processing architectures for ML tasks, accelerators for low-resource and embedded systems, and neural architectures;
• Security and privacy in distributed systems;
• Self-* capabilities in distributed systems;
• Heterogeneous and hierarchical distributed parallel systems.

Prof. Dr. Stefan Bosse
Guest Editor

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