AceFL: Federated Learning Accelerating in 6G-enabled Mobile Edge Computing Networks – IEEE
Heterogeneity among distributed edge devices and the limitation of resources may degrade the training efficiency of Federated Edge Learning over 6G-enabled mobile edge computing (MEC) networks. Taking this challenge into account, a novel federated learning scheme is proposed in this paper to accelerate the training process.
Áika: A Distributed Edge System for AI Inference – MDPI
In this paper, we present Áika, a robust system for executing distributed
Artificial Intelligence (AI) applications on the edge. Áika provides engineers and researchers with
several building blocks in the form of Agents, which enable the expression of computation pipelines
and distributed applications with robustness and privacy guarantees.
Securing ArtificiaI Intelligence (SAI); The role of hardware in security of AI – ETSI
ETSI’s newly published report gives an overview of the roles of general-purpose and specialized hardware, such as neural processors and neural networks, in enabling the security of AI. The report identifies hardware vulnerabilities and common weaknesses in AI systems and outlines the mitigations available in hardware to prevent attacks, as well as the general requirements on hardware to support the security of AI (SAI).
Oppo: 6G AI-Cube Intelligent Networking
In this newly-released white paper from the Oppo Research Institute, the authors posit the need for an additional plane in 6G telecoms networks, beyond the control and user planes. This additional plane would be the AI functional plane.
The white paper outlines the role, functions and integration of the proposed AI plane in a well-illustrated 20-page document.
Quantum Machine Learning: A Tutorial – ScienceDirect.com
This tutorial provides an overview of Quantum Machine Learning (QML), a relatively novel discipline that brings together concepts from Machine Learning (ML), Quantum Computing (QC) and Quantum Information (QI).
