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Cloud, Fog, or Edge: Where Should You Compute for AI?

As the digital landscape evolves, the question of where to execute computational tasks—whether on the cloud, at the edge, or within fog computing environments—becomes increasingly critical. In our recent study, we explored the performance and efficiency of these computing paradigms across various scenarios to help guide optimal decision-making. Video Encoding: The Power of the Edge Video encoding is a resource-intensive process that benefits significantly from low latency and high computational efficiency. Our research found that edge devices, particularly the latest generation of single-board computers like the Raspberry Pi 4 and Jetson Nano, excel in video-on-demand encoding. These devices reduce raw video transfer times and perform encoding tasks more efficiently compared to older models and some cloud instances. For continuous live stream encoding, cloud resources prove advantageous due to their lower encoding times, despite the potential for higher raw video transfer times. Cloud

Revolutionizing Healthcare with Decentralized Machine Learning using Edge AI: A New Era for Electronic Health Records

In the evolving landscape of healthcare technology, decentralized machine learning is emerging as a transformative force. A recent paper by Dragi Kimovski, titled "Decentralized Machine Learning for Intelligent Health-Care Systems on the Computing Continuum" delves into this innovative approach, proposing a novel way to enhance electronic personal health records (EHRs). The Problem with Traditional EHR Systems Traditional EHR systems, while revolutionary in their own right, come with significant limitations. Centrally managed, these systems often face issues such as a single point of failure, limited transparency in diagnostic support, and inefficiencies in utilizing the vast amounts of data generated by personal medical devices. These challenges hinder the potential for comprehensive and responsive healthcare solutions. A Decentralized Edge AI EHR Solution The authors propose a decentralized EHR system that leverages machine learning across distributed ledgers. This approach