11,992 research outputs found
JALAD: Joint Accuracy- and Latency-Aware Deep Structure Decoupling for Edge-Cloud Execution
Recent years have witnessed a rapid growth of deep-network based services and
applications. A practical and critical problem thus has emerged: how to
effectively deploy the deep neural network models such that they can be
executed efficiently. Conventional cloud-based approaches usually run the deep
models in data center servers, causing large latency because a significant
amount of data has to be transferred from the edge of network to the data
center. In this paper, we propose JALAD, a joint accuracy- and latency-aware
execution framework, which decouples a deep neural network so that a part of it
will run at edge devices and the other part inside the conventional cloud,
while only a minimum amount of data has to be transferred between them. Though
the idea seems straightforward, we are facing challenges including i) how to
find the best partition of a deep structure; ii) how to deploy the component at
an edge device that only has limited computation power; and iii) how to
minimize the overall execution latency. Our answers to these questions are a
set of strategies in JALAD, including 1) A normalization based in-layer data
compression strategy by jointly considering compression rate and model
accuracy; 2) A latency-aware deep decoupling strategy to minimize the overall
execution latency; and 3) An edge-cloud structure adaptation strategy that
dynamically changes the decoupling for different network conditions.
Experiments demonstrate that our solution can significantly reduce the
execution latency: it speeds up the overall inference execution with a
guaranteed model accuracy loss.Comment: conference, copyright transfered to IEE
6G White Paper on Machine Learning in Wireless Communication Networks
The focus of this white paper is on machine learning (ML) in wireless
communications. 6G wireless communication networks will be the backbone of the
digital transformation of societies by providing ubiquitous, reliable, and
near-instant wireless connectivity for humans and machines. Recent advances in
ML research has led enable a wide range of novel technologies such as
self-driving vehicles and voice assistants. Such innovation is possible as a
result of the availability of advanced ML models, large datasets, and high
computational power. On the other hand, the ever-increasing demand for
connectivity will require a lot of innovation in 6G wireless networks, and ML
tools will play a major role in solving problems in the wireless domain. In
this paper, we provide an overview of the vision of how ML will impact the
wireless communication systems. We first give an overview of the ML methods
that have the highest potential to be used in wireless networks. Then, we
discuss the problems that can be solved by using ML in various layers of the
network such as the physical layer, medium access layer, and application layer.
Zero-touch optimization of wireless networks using ML is another interesting
aspect that is discussed in this paper. Finally, at the end of each section,
important research questions that the section aims to answer are presented
Algebraic Watchdog: Mitigating Misbehavior in Wireless Network Coding
We propose a secure scheme for wireless network coding, called the algebraic
watchdog. By enabling nodes to detect malicious behaviors probabilistically and
use overheard messages to police their downstream neighbors locally, the
algebraic watchdog delivers a secure global self-checking network. Unlike
traditional Byzantine detection protocols which are receiver-based, this
protocol gives the senders an active role in checking the node downstream. The
key idea is inspired by Marti et al.'s watchdog-pathrater, which attempts to
detect and mitigate the effects of routing misbehavior.
As an initial building block of a such system, we first focus on a two-hop
network. We present a graphical model to understand the inference process nodes
execute to police their downstream neighbors; as well as to compute, analyze,
and approximate the probabilities of misdetection and false detection. In
addition, we present an algebraic analysis of the performance using an
hypothesis testing framework that provides exact formulae for probabilities of
false detection and misdetection.
We then extend the algebraic watchdog to a more general network setting, and
propose a protocol in which we can establish trust in coded systems in a
distributed manner. We develop a graphical model to detect the presence of an
adversarial node downstream within a general multi-hop network. The structure
of the graphical model (a trellis) lends itself to well-known algorithms, such
as the Viterbi algorithm, which can compute the probabilities of misdetection
and false detection. We show analytically that as long as the min-cut is not
dominated by the Byzantine adversaries, upstream nodes can monitor downstream
neighbors and allow reliable communication with certain probability. Finally,
we present simulation results that support our analysis.Comment: 10 pages, 10 figures, Submitted to IEEE Journal on Selected Areas in
Communications (JSAC) "Advances in Military Networking and Communications
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