314,710 research outputs found
Wireless for Machine Learning
As data generation increasingly takes place on devices without a wired
connection, Machine Learning over wireless networks becomes critical. Many
studies have shown that traditional wireless protocols are highly inefficient
or unsustainable to support Distributed Machine Learning. This is creating the
need for new wireless communication methods. In this survey, we give an
exhaustive review of the state of the art wireless methods that are
specifically designed to support Machine Learning services. Namely,
over-the-air computation and radio resource allocation optimized for Machine
Learning. In the over-the-air approach, multiple devices communicate
simultaneously over the same time slot and frequency band to exploit the
superposition property of wireless channels for gradient averaging
over-the-air. In radio resource allocation optimized for Machine Learning,
Active Learning metrics allow for data evaluation to greatly optimize the
assignment of radio resources. This paper gives a comprehensive introduction to
these methods, reviews the most important works, and highlights crucial open
problems.Comment: Corrected typo in author name. From the incorrect Maitron to the
correct Mairto
Edge Learning for 6G-enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses
The ongoing deployment of the fifth generation (5G) wireless networks
constantly reveals limitations concerning its original concept as a key driver
of Internet of Everything (IoE) applications. These 5G challenges are behind
worldwide efforts to enable future networks, such as sixth generation (6G)
networks, to efficiently support sophisticated applications ranging from
autonomous driving capabilities to the Metaverse. Edge learning is a new and
powerful approach to training models across distributed clients while
protecting the privacy of their data. This approach is expected to be embedded
within future network infrastructures, including 6G, to solve challenging
problems such as resource management and behavior prediction. This survey
article provides a holistic review of the most recent research focused on edge
learning vulnerabilities and defenses for 6G-enabled IoT. We summarize the
existing surveys on machine learning for 6G IoT security and machine
learning-associated threats in three different learning modes: centralized,
federated, and distributed. Then, we provide an overview of enabling emerging
technologies for 6G IoT intelligence. Moreover, we provide a holistic survey of
existing research on attacks against machine learning and classify threat
models into eight categories, including backdoor attacks, adversarial examples,
combined attacks, poisoning attacks, Sybil attacks, byzantine attacks,
inference attacks, and dropping attacks. In addition, we provide a
comprehensive and detailed taxonomy and a side-by-side comparison of the
state-of-the-art defense methods against edge learning vulnerabilities.
Finally, as new attacks and defense technologies are realized, new research and
future overall prospects for 6G-enabled IoT are discussed
Deep Reinforcement Learning for Control of Microgrids: A Review
A microgrid is widely accepted as a prominent solution to enhance resilience and performance in distributed power systems. Microgrids are flexible for adding distributed energy resources in the ecosystem of the electrical networks. Control techniques are used to synchronize distributed energy resources (DERs) due to their turbulent nature. DERs including alternating current, direct current and hybrid load with storage systems have been used in microgrids quite frequently due to which controlling the flow of energy in microgrids have been complex task with traditional control approaches. Distributed as well central approach to apply control algorithms is well-known methods to regulate frequency and voltage in microgrids. Recently techniques based of artificial intelligence are being applied for the problems that arise in operation and control of latest generation microgrids and smart grids. Such techniques are categorized in machine learning and deep learning in broader terms. The objective of this research is to survey the latest strategies of control in microgrids using the deep reinforcement learning approach (DRL). Other techniques of artificial intelligence had already been reviewed extensively but the use of DRL has increased in the past couple of years. To bridge the gap for the researchers, this survey paper is being presented with a focus on only Microgrids control DRL techniques for voltage control and frequency regulation with distributed, cooperative and multi agent approaches are presented in this research
Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges
Federated learning is a machine learning paradigm that emerges as a solution
to the privacy-preservation demands in artificial intelligence. As machine
learning, federated learning is threatened by adversarial attacks against the
integrity of the learning model and the privacy of data via a distributed
approach to tackle local and global learning. This weak point is exacerbated by
the inaccessibility of data in federated learning, which makes harder the
protection against adversarial attacks and evidences the need to furtherance
the research on defence methods to make federated learning a real solution for
safeguarding data privacy. In this paper, we present an extensive review of the
threats of federated learning, as well as as their corresponding
countermeasures, attacks versus defences. This survey provides a taxonomy of
adversarial attacks and a taxonomy of defence methods that depict a general
picture of this vulnerability of federated learning and how to overcome it.
Likewise, we expound guidelines for selecting the most adequate defence method
according to the category of the adversarial attack. Besides, we carry out an
extensive experimental study from which we draw further conclusions about the
behaviour of attacks and defences and the guidelines for selecting the most
adequate defence method according to the category of the adversarial attack.
This study is finished leading to meditated learned lessons and challenges
The use of neural networks to probe the structure of the nearby universe
In the framework of the European VO-Tech project, we are implementing new machine learning methods specifically tailored to match the needs of astronomical data mining. In this paper, we shortly present the methods and discuss an application to the Sloan Digital Sky Survey public data set. In particular, we discuss some preliminary results on the 3-D taxonomy of the nearby (z < 0.5) universe. Using neural networks trained on the available spectroscopic base of knowledge we derived distance estimates for ca. 30 million galaxies distributed over 8,000 sq. deg. We also use unsupervised clustering tools to investigate whether it is possible to characterize in broad morphological bins the nature of each object and produce a reliable list of candidate AGNs and QSOs
The use of neural networks to probe the structure of the nearby universe
In the framework of the European VO-Tech project, we are implementing new machine learning methods specifically tailored to match the needs of astronomical data mining. In this paper, we shortly present the methods and discuss an application to the Sloan Digital Sky Survey public data set. In particular, we discuss some preliminary results on the 3-D taxonomy of the nearby (z < 0.5) universe. Using neural networks trained on the available spectroscopic base of knowledge we derived distance estimates for ca. 30 million galaxies distributed over 8,000 sq. deg. We also use unsupervised clustering tools to investigate whether it is possible to characterize in broad morphological bins the nature of each object and produce a reliable list of candidate AGNs and QSOs
Architectures and synchronization techniques for distributed satellite systems: a survey
Cohesive Distributed Satellite Systems (CDSSs) is a key enabling technology for the future of remote sensing and communication missions. However, they have to meet strict synchronization requirements before their use is generalized. When clock or local oscillator signals are generated locally at each of the distributed nodes, achieving exact synchronization in absolute phase, frequency, and time is a complex problem. In addition, satellite systems have significant resource constraints, especially for small satellites, which are envisioned to be part of the future CDSSs. Thus, the development of precise, robust, and resource-efficient synchronization techniques is essential for the advancement of future CDSSs. In this context, this survey aims to summarize and categorize the most relevant results on synchronization techniques for Distributed Satellite Systems (DSSs). First, some important architecture and system concepts are defined. Then, the synchronization methods reported in the literature are reviewed and categorized. This article also provides an extensive list of applications and examples of synchronization techniques for DSSs in addition to the most significant advances in other operations closely related to synchronization, such as inter-satellite ranging and relative position. The survey also provides a discussion on emerging data-driven synchronization techniques based on Machine Learning (ML). Finally, a compilation of current research activities and potential research topics is proposed, identifying problems and open challenges that can be useful for researchers in the field.This work was supported by the Luxembourg National Research Fund (FNR), through the CORE Project COHEsive SATellite (COHESAT): Cognitive Cohesive Networks of Distributed Units for Active and Passive Space Applications, under Grant FNR11689919.Award-winningPostprint (published version
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