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Found 7 papers, 0 papers with code
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.
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.
