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Found 23 papers, 2 papers with code
Adapting CSI-Guided Imaging Across Diverse Environments: An Experimental Study Leveraging Continuous Learning
This study explores the feasibility of adapting CSI-guided imaging across varied environments.
$Λ$-Split: A Privacy-Preserving Split Computing Framework for Cloud-Powered Generative AI
To mitigate these concerns, we introduce $\Lambda$-Split, a split computing framework to facilitate computational offloading while simultaneously fortifying data privacy against risks such as eavesdropping and unauthorized access.
Tram-FL: Routing-based Model Training for Decentralized Federated Learning
In decentralized federated learning (DFL), substantial traffic from frequent inter-node communication and non-independent and identically distributed (non-IID) data challenges high-accuracy model acquisition.
CSI-Inpainter: Enabling Visual Scene Recovery from CSI Time Sequences for Occlusion Removal
Introducing CSI-Inpainter, a pioneering approach for occlusion removal using Channel State Information (CSI) time sequences, this work propels the application of wireless signal processing into the realm of visual scene recovery.
Point Cloud-based Proactive Link Quality Prediction for Millimeter-wave Communications
The experimental results showed that our proposed method can predict future large attenuation of mmWave received signal strength and throughput induced by the LOS path blockage by pedestrians with comparable or superior accuracy to image-based prediction methods.
Neural Architecture Search for Improving Latency-Accuracy Trade-off in Split Computing
This paper proposes a neural architecture search (NAS) method for split computing.
Vision-Aided Frame-Capture-Based CSI Recomposition for WiFi Sensing: A Multimodal Approach
In this study, we propose the use of camera images toward the accuracy enhancement of CSI recomposition from BFM.
Watch from sky: machine-learning-based multi-UAV network for predictive police surveillance
This paper presents the watch-from-sky framework, where multiple unmanned aerial vehicles (UAVs) play four roles, i. e., sensing, data forwarding, computing, and patrolling, for predictive police surveillance.
Communication-oriented Model Fine-tuning for Packet-loss Resilient Distributed Inference under Highly Lossy IoT Networks
However, generally, there is a communication system-level trade-off between communication latency and reliability; thus, to provide accurate DI results, a reliable and high-latency communication system is required to be adapted, which results in non-negligible end-to-end latency of the DI.
Frame-Capture-Based CSI Recomposition Pertaining to Firmware-Agnostic WiFi Sensing
This indicates that WiFi sensing that leverages the BFM matrix is more practical to implement using the pre-installed APs.
Beamforming Feedback-based Model-Driven Angle of Departure Estimation Toward Legacy Support in WiFi Sensing: An Experimental Study
Moreover, the evaluations performed in this study revealed that the BFF-based MUSIC achieved a comparable error of AoD estimation to the CSI-based MUSIC, while BFF is a highly compressed version of CSI in IEEE 802. 11ac/ax.
Packet-Loss-Tolerant Split Inference for Delay-Sensitive Deep Learning in Lossy Wireless Networks
This study solves the problem of the incremental retransmission latency caused by packet loss in a lossy IoT network.
Decentralized and Model-Free Federated Learning: Consensus-Based Distillation in Function Space
Because FL algorithms hardly converge the parameters of machine learning (ML) models, this paper focuses on the convergence of ML models in function spaces.
Zero-Shot Adaptation for mmWave Beam-Tracking on Overhead Messenger Wires through Robust Adversarial Reinforcement Learning
Second, we demonstrate the feasibility of \textit{zero-shot adaptation} as a solution, where a learning agent adapts to environmental parameters unseen during training.
When Wireless Communications Meet Computer Vision in Beyond 5G
This article articulates the emerging paradigm, sitting at the confluence of computer vision and wireless communication, to enable beyond-5G/6G mission-critical applications (autonomous/remote-controlled vehicles, visuo-haptic VR, and other cyber-physical applications).
MAB-based Client Selection for Federated Learning with Uncertain Resources in Mobile Networks
This paper proposes a multi-armed bandit (MAB)-based client selection method to solve the exploration and exploitation trade-off and reduce the time consumption for FL in mobile networks.
Networking and Internet Architecture
Estimation of Individual Device Contributions for Incentivizing Federated Learning
Federated learning (FL) is an emerging technique used to train a machine-learning model collaboratively using the data and computation resource of the mobile devices without exposing privacy-sensitive user data.
Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training with Non-IID Private Data
To this end, based on the idea of leveraging an unlabeled open dataset, we propose a distillation-based semi-supervised FL (DS-FL) algorithm that exchanges the outputs of local models among mobile devices, instead of model parameter exchange employed by the typical frameworks.
Lottery Hypothesis based Unsupervised Pre-training for Model Compression in Federated Learning
The key idea of the proposed method is to obtain a ``good'' subnetwork from the original NN using the unlabeled data based on the lottery hypothesis.
Differentially Private AirComp Federated Learning with Power Adaptation Harnessing Receiver Noise
To this end, a differentially private AirComp-based FL is designed in this study, where the key idea is to harness receiver noise perturbation injected to aggregated global models inherently, thereby preventing the inference of clients' private data.
Networking and Internet Architecture Signal Processing
Deep Reinforcement Learning-Based Channel Allocation for Wireless LANs with Graph Convolutional Networks
In densely deployed WLANs, the number of the available topologies of APs is extremely high, and thus we extract the features of the topological structures based on GCNs.
Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID Data
Therefore, to mitigate the degradation induced by non-IID data, we assume that a limited number (e. g., less than 1%) of clients allow their data to be uploaded to a server, and we propose a hybrid learning mechanism referred to as Hybrid-FL, wherein the server updates the model using the data gathered from the clients and aggregates the model with the models trained by clients.
Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
Specifically, FedCS solves a client selection problem with resource constraints, which allows the server to aggregate as many client updates as possible and to accelerate performance improvement in ML models.
