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Found 25 papers, 2 papers with code
Socialized Learning: A Survey of the Paradigm Shift for Edge Intelligence in Networked Systems
In response to these issues, this paper introduces socialized learning (SL) as a promising solution, further propelling the advancement of EI.
Collaborative Ground-Space Communications via Evolutionary Multi-objective Deep Reinforcement Learning
Specifically, it is found that DCB enables terminals that cannot reach the uplink achievable threshold to achieve efficient direct uplink transmission, which thus reveals that DCB is an effective solution for enabling direct ground-space communications.
Communication-Efficient Large-Scale Distributed Deep Learning: A Comprehensive Survey
Due to intensive synchronization of models and sharing of data across GPUs and computing nodes during distributed training and inference processes, communication efficiency becomes the bottleneck for achieving high performance at a large scale.
TJCCT: A Two-timescale Approach for UAV-assisted Mobile Edge Computing
Since the problem is a non-convex and NP-hard mixed integer nonlinear programming (MINLP), we propose a two-timescale joint computing resource allocation, computation offloading, and trajectory control (TJCCT) approach for solving the problem.
On Designing Multi-UAV aided Wireless Powered Dynamic Communication via Hierarchical Deep Reinforcement Learning
We then propose a new multi-agent based hierarchical deep reinforcement learning (MAHDRL) framework with two tiers to solve the problem efficiently, where the soft actor critic (SAC) policy is designed in tier-1 to determine each UAV's continuous trajectory and binary WET decision over time slots, and the deep-Q learning (DQN) policy is designed in tier-2 to determine each UAV's binary WDC decisions over sub-slots under the given UAV trajectory from tier-1.
A Survey of Adversarial CAPTCHAs on its History, Classification and Generation
The discovery of adversarial examples provides an ideal solution to the security and usability trade-off by integrating adversarial examples and CAPTCHAs to generate adversarial CAPTCHAs that can fool the deep models.
UAV Swarm-enabled Collaborative Secure Relay Communications with Time-domain Colluding Eavesdropper
Unmanned aerial vehicles (UAVs) as aerial relays are practically appealing for assisting Internet of Things (IoT) network.
Joint Task Offloading and Resource Allocation in Aerial-Terrestrial UAV Networks with Edge and Fog Computing for Post-Disaster Rescue
Unmanned aerial vehicles (UAVs) play an increasingly important role in assisting fast-response post-disaster rescue due to their fast deployment, flexible mobility, and low cost.
Terahertz Communications and Sensing for 6G and Beyond: A Comprehensive View
Different from earlier surveys, this paper presents a comprehensive treatment and technology survey on THz communications and sensing in terms of the advantages, applications, propagation characterization, channel modeling, measurement campaigns, antennas, transceiver devices, beamforming, networking, the integration of communications and sensing, and experimental testbeds.
Improving Rating Prediction in Multi-Criteria Recommender Systems via a Collective Factor Model
Experiments on three real-world datasets show that our proposed architecture achieves up to 13. 14% lower prediction error over baseline approaches.
Accelerating Wireless Federated Learning via Nesterov's Momentum and Distributed Principle Component Analysis
A wireless federated learning system is investigated by allowing a server and workers to exchange uncoded information via orthogonal wireless channels.
DeepMA: End-to-end Deep Multiple Access for Wireless Image Transmission in Semantic Communication
Semantic communication is a new paradigm that exploits deep learning models to enable end-to-end communications processes, and recent studies have shown that it can achieve better noise resiliency compared with traditional communication schemes in a low signal-to-noise (SNR) regime.
When Quantum Information Technologies Meet Blockchain in Web 3.0
Therefore, in this paper, we introduce a quantum blockchain-driven Web 3. 0 framework that provides information-theoretic security for decentralized data transferring and payment transactions.
AI-aided Traffic Control Scheme for M2M Communications in the Internet of Vehicles
Due to the rapid growth of data transmissions in internet of vehicles (IoV), finding schemes that can effectively alleviate access congestion has become an important issue.
Power Line Communication and Sensing Using Time Series Forecasting
Power line communication (PLC) offers a cost-effective solution for joint communication and sensing for smart grids.
Cooperative Multi-Agent Reinforcement Learning Based Distributed Dynamic Spectrum Access in Cognitive Radio Networks
With the development of the 5G and Internet of Things, amounts of wireless devices need to share the limited spectrum resources.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
Blockchain-empowered Data-driven Networks: A Survey and Outlook
The paths leading to future networks are pointing towards a data-driven paradigm to better cater to the explosive growth of mobile services as well as the increasing heterogeneity of mobile devices, many of which generate and consume large volumes and variety of data.
Networking and Internet Architecture
Tailored Learning-Based Scheduling for Kubernetes-Oriented Edge-Cloud System
In this paper, we introduce KaiS, a learning-based scheduling framework for such edge-cloud systems to improve the long-term throughput rate of request processing.
Energy-Efficient Resource Allocation for NOMA enabled MEC Networks with Imperfect CSI
In this paper, we mainly focus on energy-efficient resource allocation for a multi-user, multi-BS NOMA assisted MEC network with imperfect channel state information (CSI), in which each user can upload its tasks to multiple base stations (BSs) for remote executions.
Edge Network-Assisted Real-Time Object Detection Framework for Autonomous Driving
In an EODF, AVs extract the region of interests~(RoIs) of the captured image when the channel quality is not sufficiently good for supporting real-time OD.
Communication-Efficient Robust Federated Learning Over Heterogeneous Datasets
For strongly convex loss functions, FRPG and LFRPG have provably faster convergence rates than a benchmark robust stochastic aggregation algorithm.
Energy Efficient User Clustering, Hybrid Precoding and Power Optimization in Terahertz MIMO-NOMA Systems
Considering the power consumption and implementation complexity, the hybrid precoding scheme based on the sub-connection structure is adopted.
Emotion Recognition From Gait Analyses: Current Research and Future Directions
This article reviews current research on gait-based emotion detection, particularly on how gait parameters can be affected by different emotion states and how the emotion states can be recognized through distinct gait patterns.
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
Ubiquitous sensors and smart devices from factories and communities are generating massive amounts of data, and ever-increasing computing power is driving the core of computation and services from the cloud to the edge of the network.
Secure Distributed On-Device Learning Networks With Byzantine Adversaries
The worst-case malfunctioning terminals are the Byzantine adversaries, that can perform arbitrary harmful operations to compromise the learned model based on the full knowledge of the networks.
