18 research outputs found
Decentralized and Model-Free Federated Learning: Consensus-Based Distillation in Function Space
This paper proposes a fully decentralized federated learning (FL) scheme for
Internet of Everything (IoE) devices that are connected via multi-hop networks.
Because FL algorithms hardly converge the parameters of machine learning (ML)
models, this paper focuses on the convergence of ML models in function spaces.
Considering that the representative loss functions of ML tasks e.g, mean
squared error (MSE) and Kullback-Leibler (KL) divergence, are convex
functionals, algorithms that directly update functions in function spaces could
converge to the optimal solution. The key concept of this paper is to tailor a
consensus-based optimization algorithm to work in the function space and
achieve the global optimum in a distributed manner. This paper first analyzes
the convergence of the proposed algorithm in a function space, which is
referred to as a meta-algorithm, and shows that the spectral graph theory can
be applied to the function space in a manner similar to that of numerical
vectors. Then, consensus-based multi-hop federated distillation (CMFD) is
developed for a neural network (NN) to implement the meta-algorithm. CMFD
leverages knowledge distillation to realize function aggregation among adjacent
devices without parameter averaging. An advantage of CMFD is that it works even
with different NN models among the distributed learners. Although CMFD does not
perfectly reflect the behavior of the meta-algorithm, the discussion of the
meta-algorithm's convergence property promotes an intuitive understanding of
CMFD, and simulation evaluations show that NN models converge using CMFD for
several tasks. The simulation results also show that CMFD achieves higher
accuracy than parameter aggregation for weakly connected networks, and CMFD is
more stable than parameter aggregation methods.Comment: submitted to IEEE TSIP
Deep-Reinforcement-Learning-Based Distributed Vehicle Position Controls for Coverage Expansion in mmWave V2X
Concurrent Transmission Scheduling for Perceptual Data Sharing in mmWave Vehicular Networks
RW-QAnswer: an assisting system for intelligent environments using semantic technology
International audienc
AMoND: Area-Controlled Mobile Ad-Hoc Networking With Digital Twin
Future smart cities are expected to provide intelligent services such as predictions, detections, and automation through digital twins. However, the creation of digital twins requires the processing of an enormous amount of data, thereby leading to an increase in mobile network traffic. This traffic is produced by applications in user devices and city services, which engage in local consumption at the city scale through sensor and camera devices using mobile networks. Such increased traffic can compromise the communication speed and stability. To alleviate this burden, traffic offloading becomes a crucial consideration in the beyond-5G era. This paper presents a scheme known as Area-Controlled Mobile Ad-Hoc Networking (AMoND). AMoND uses a hierarchical structure of a location layer and an ad-hoc layer to construct area-controlled mobile ad-hoc networks (MANETs) for mutual support of the digital twin and MANETs. AMoND effectively suppresses mobile network traffic by harnessing the digital twin to assist the MANETs during data collection for the digital twin construction. Importantly, the digital twin used in AMoND focuses on the management of node location information and does not need to reproduce the real space on a computer fully. AMoND is not dependent on a specific MANET protocol and can be used as an add-on. AMoND exhibits the ability to reduce traffic volumes by up to approximately 65%, while maintaining arrival rates that are comparable to existing MANET protocols under certain conditions
Vision-Aided Frame-Capture-Based CSI Recomposition for WiFi Sensing: A Multimodal Approach
Recompositing channel state information (CSI) from the beamforming feedback
matrix (BFM), which is a compressed version of CSI and can be captured because
of its lack of encryption, is an alternative way of implementing
firmware-agnostic WiFi sensing. In this study, we propose the use of camera
images toward the accuracy enhancement of CSI recomposition from BFM. The key
motivation for this vision-aided CSI recomposition is to draw a first-hand
insight that the BFM does not fully involve spatial information to recomposite
CSI and that this could be compensated by camera images. To leverage the camera
images, we use multimodal deep learning, where the two modalities, i.e., images
and BFMs, are integrated to recomposite the CSI. We conducted experiments using
IEEE 802.11ac devices. The experimental results confirmed that the
recomposition accuracy of the proposed multimodal framework is improved
compared to the single-modal framework only using images or BFMs