1,721,045 research outputs found
Asymptotic Learning Requirements for Stealth Attacks on Linearized State Estimation
No full textInformation-theoretic stealth attacks are data injection attacks that minimize the amount of information acquired by the operator about the state variables, while simultaneously limiting the Kullback-Leibler divergence between the distribution of the measurements under attack and the distribution under normal operation with the aim of controling the probability of attack detection. For Gaussian distributed state variables, attack construction requires knowledge of the second order statistics of the state variables, which is estimated from a finite number of past realizations using a sample covariance matrix. Within this framework, the attack performance is studied for the attack construction with the sample covariance matrix. This results in an analysis of the amount of data required to learn the covariance matrix of the state variables used on the attack construction. The ergodic attack performance is characterized using asymptotic random matrix theory tools and the variance of the attack performance is bounded. The ergodic performance and the variance bounds are assessed with simulations on IEEE test systems
Privacy-cost trade-offs in smart electricity metering systems
Get PDFTrade-offs between privacy and cost are studied for a smart grid consumer, whose electricity consumption is monitoredin almost real time by the utility provider (UP) through smart meter (SM) readings. It is assumed that an electrical battery isavailable to the consumer, which can be utilized both to achieve privacy and to reduce the energy cost by demand shaping.Privacy is measured via the mean squared distance between the SM readings and a target load profile, while time-of-use (ToU)pricing is considered to compute the cost incurred. The consumer can also sell electricity back to the UP to further improve theprivacy-cost trade-off. Two privacy-preserving energy management policies (EMPs) are proposed, which differ in the way the targetload profile is characterized. A more practical EMP, which optimizes the energy management less frequently, is also considered.Numerical results are presented to compare the privacy-cost trade-off of these EMPs, considering various privacy indicators
Distributed Learning in Wireless Networks: Recent Progress and Future Challenges
No full textThe next-generation of wireless networks will enable many machine learning (ML) tools and applications to efficiently analyze various types of data collected by edge devices for inference, autonomy, and decision making purposes. However, due to resource constraints, delay limitations, and privacy challenges, edge devices cannot offload their entire collected datasets to a cloud server for centrally training their ML models or inference purposes. To overcome these challenges, distributed learning and inference techniques have been proposed as a means to enable edge devices to collaboratively train ML models without raw data exchanges, thus reducing the communication overhead and latency as well as improving data privacy. However, deploying distributed learning over wireless networks faces several challenges including the uncertain wireless environment (e.g., dynamic channel and interference), limited wireless resources (e.g., transmit power and radio spectrum), and hardware resources (e.g., computational power). This paper provides a comprehensive study of how distributed learning can be efficiently and effectively deployed over wireless edge networks. We present a detailed overview of several emerging distributed learning paradigms, including federated learning, federated distillation, distributed inference, and multi-agent reinforcement learning. For each learning framework, we first introduce the motivation for deploying it over wireless networks. Then, we present a detailed literature review on the use of communication techniques for its efficient deployment. We then introduce an illustrative example to show how to optimize wireless networks to improve its performance. Finally, we introduce future research opportunities. In a nutshell, this paper provides a holistic set of guidelines on how to deploy a broad range of distributed learning frameworks over real-world wireless communication networks
Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge
No full textWe study federated learning (FL) at the wireless edge, where power-limited devices with local datasets collaboratively train a joint model with the help of a remote parameter server (PS). We assume that the devices are connected to the PS through a bandwidth-limited shared wireless channel. At each iteration of FL, a subset of the devices are scheduled to transmit their local model updates to the PS over orthogonal channel resources, while each participating device must compress its model update to accommodate to its link capacity. We design novel scheduling and resource allocation policies that decide on the subset of the devices to transmit at each round, and how the resources should be allocated among the participating devices, not only based on their channel conditions, but also on the significance of their local model updates. We then establish convergence of a wireless FL algorithm with device scheduling, where devices have limited capacity to convey their messages. The results of numerical experiments show that the proposed scheduling policy, based on both the channel conditions and the significance of the local model updates, provides a better long-term performance than scheduling policies based only on either of the two metrics individually. Furthermore, we observe that when the data is independent and identically distributed (i.i.d.) across devices, selecting a single device at each round provides the best performance, while when the data distribution is non-i.i.d., scheduling multiple devices at each round improves the performance. This observation is verified by the convergence result, which shows that the number of scheduled devices should increase for a less diverse and more biased data distribution
Review and Perspectives of Micro/Nano Technologies as Key-Enablers of 6G
Get PDFTo date, 5G (5th generation of mobile communications) roll out has been going on for more than two years, and the most of it has still to come. Meanwhile, Key Performance Indicators (KPIs) and Key Enabling Technologies (KETs) of Beyond-5G (B5G) and 6G (6th generation of mobile communications) are already at stake, looking at 2030. Future networks will leverage autonomous and evolutionary characteristics, triggered by the cornerstone of Artificial Intelligence (AI), falling well-beyond the scopes of 5G. Besides, seamless increase of KPIs, across the transition from 5G to 6G, with 100-1000 times higher data rate per user, latency reduction and reliability improvement, also stepping into the domain of (sub-)THz and optical communications, will set unparalleled demands for Hardware (HW) systems and components. This work focuses on the envisaged gap existing between currently in use strategies for design of Hardware-Software (HW-SW) systems and what the AI-driven 6G will demand, in terms of adaptivity, flexibility and evolution. An important part is forecasted for Micro/Nano technologies, devices and systems, in enabling 6G functionalities, especially at the network edge, stimulating partial reconceptualization of the classical idea of HW, in fact, rising its level of abstraction
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Optimal demand-side management for joint privacy-cost optimization with energy storage
No full textThe smart meter (SM) privacy problem is addressed together with the cost of energy for the user. It is assumed that a storage device, e.g., an electrical battery, is available to the user, which can be utilized both to achieve privacy and to reduce the energy cost by modifying the energy consumption profile. Privacy is measured via the mean squared-error between the SM readings, which are reported to the utility provider (UP), and a target load; while time-of-use pricing is considered for energy cost calculation. The optimal trade-off between the achievable privacy and the energy cost is characterized by taking into account the limited capacity of the battery as well as the capability to sell energy to the UP. Extensive numerical simulations are presented to evaluate the performance of the proposed strategy for different system settings
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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