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Found 77 papers, 6 papers with code
Content Popularity Prediction Based on Quantized Federated Bayesian Learning in Fog Radio Access Networks
To utilize the computing resources of other fog access points (F-APs) and to reduce the communications overhead, we propose a quantized federated learning (FL) framework combining with Bayesian learning.
Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition
However, for machine learning-based traffic sign recognition on the Internet of Vehicles (IoV), a large amount of traffic sign data from distributed vehicles is needed to be gathered in a centralized server for model training, which brings serious privacy leakage risk because of traffic sign data containing lots of location privacy information.
Reconfigurable Intelligent Surface-Aided Joint Radar and Covert Communications: Fundamentals, Optimization, and Challenges
Thus, we first apply covert communication into JRC and propose a joint radar and covert communication (JRCC) system to achieve high spectrum utilization and secure data transmission simultaneously.
Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT
In this paper, we propose FedGS, which is a hierarchical cloud-edge-end FL framework for 5G empowered industries, to improve industrial FL performance on non-i. i. d.
Heterogeneous Federated Learning via Grouped Sequential-to-Parallel Training
In this paper, we propose a data heterogeneity-robust FL approach, FedGSP, to address this challenge by leveraging on a novel concept of dynamic Sequential-to-Parallel (STP) collaborative training.
Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Drones
This emerging technology relies on sharing ground truth labeled data between Unmanned Aerial Vehicle (UAV) swarms to train a high-quality automatic image recognition model.
Reconfigurable Holographic Surfaces for Future Wireless Communications
Future wireless communications look forward to constructing a ubiquitous intelligent information network with high data rates through cost-efficient devices.
Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective
In this paper, we provide a comprehensive review for the economic and game theoretic approaches proposed in the literature to design various schemes for stimulating data owners to participate in FL training process.
Semi-Supervised Federated Learning with non-IID Data: Algorithm and System Design
In this paper, we present a robust semi-supervised FL system design, where the system aims to solve the problem of data availability and non-IID in FL.
Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey
Then, we review a number of applications of MARL to solve emerging issues in future Internet.
Economics of Semantic Communication System in Wireless Powered Internet of Things
The semantic communication system enables wireless devices to communicate effectively with the semantic meaning of the data.
Spectrum Learning-Aided Reconfigurable Intelligent Surfaces for 'Green' 6G Networks
In the sixth-generation (6G) era, emerging large-scale computing based applications (for example processing enormous amounts of images in real-time in autonomous driving) tend to lead to excessive energy consumption for the end users, whose devices are usually energy-constrained.
6G Internet of Things: A Comprehensive Survey
The sixth generation (6G) wireless communication networks are envisioned to revolutionize customer services and applications via the Internet of Things (IoT) towards a future of fully intelligent and autonomous systems.
Risk Adversarial Learning System for Connected and Autonomous Vehicle Charging
To tackle this, we formulate an RDSS problem for the DSO, where the objective is to maximize the charging capacity utilization by satisfying the laxity risk of the DSO.
Optimal Power Allocation for Rate Splitting Communications with Deep Reinforcement Learning
This letter introduces a novel framework to optimize the power allocation for users in a Rate Splitting Multiple Access (RSMA) network.
Federated Learning for Industrial Internet of Things in Future Industries
The Industrial Internet of Things (IIoT) offers promising opportunities to transform the operation of industrial systems and becomes a key enabler for future industries.
Transferable Deep Reinforcement Learning Framework for Autonomous Vehicles with Joint Radar-Data Communications
Autonomous Vehicles (AVs) are required to operate safely and efficiently in dynamic environments.
A Joint Energy and Latency Framework for Transfer Learning over 5G Industrial Edge Networks
In this paper, we propose a transfer learning (TL)-enabled edge-CNN framework for 5G industrial edge networks with privacy-preserving characteristic.
Optimizing the Long-Term Average Reward for Continuing MDPs: A Technical Report
cost), by integrating R-learning, a tabular reinforcement learning (RL) algorithm tailored for maximizing the long-term average reward, and traditional DRL algorithms, initially developed to optimize the discounted long-term cumulative reward rather than the average one.
Dynamic Network Service Selection in Intelligent Reflecting Surface-Enabled Wireless Systems: Game Theory Approaches
To formulate the SP and network service selection, we adopt an evolutionary game in which the users are able to adapt their network selections depending on the utilities that they achieve.
Computer Science and Game Theory
Digital-Twin-Enabled 6G: Vision, Architectural Trends, and Future Directions
Therefore, enabling IoE applications over 6G requires a new framework that can be used to manage, operate, and optimize the 6G wireless system and its underlying IoE services.
Edge-computing
Networking and Internet Architecture
Transfer Learning for Future Wireless Networks: A Comprehensive Survey
Finally, we highlight important challenges, open issues, and future research directions of TL in future wireless networks.
Ambient Backscatter-Assisted Wireless-Powered Relaying
With different mode selection protocols, we characterize the success probability and ergodic capacity of a dual-hop relaying system with the hybrid relay in the field of randomly located ambient transmitters.
Information Theory Networking and Internet Architecture Information Theory
Intelligent Reflecting Surface Assisted Anti-Jamming Communications Based on Reinforcement Learning
Aiming to enhance the communication performance against smart jammer, an optimization problem for jointly optimizing power allocation at the base station (BS) and reflecting beamforming at the IRS is formulated.
A Comprehensive Survey of 6G Wireless Communications
First, we give an overview of 6G from perspectives of technologies, security and privacy, and applications.
Resource Allocation for Intelligent Reflecting Surface Aided Cooperative Communications
This paper investigates an intelligent reflecting surface (IRS) aided cooperative communication network, where the IRS exploits large reflecting elements to proactively steer the incident radio-frequency wave towards destination terminals (DTs).
Towards Communication-efficient and Attack-Resistant Federated Edge Learning for Industrial Internet of Things
Second, we propose an asynchronous local differential privacy mechanism, which improves communication efficiency and mitigates gradient leakage attacks by adding well-designed noise to the gradients of edge nodes.
Poisoning Semi-supervised Federated Learning via Unlabeled Data: Attacks and Defenses
Extensive case studies have shown that our attacks are effective on different datasets and common semi-supervised learning methods.
Optimization-driven Machine Learning for Intelligent Reflecting Surfaces Assisted Wireless Networks
Due to the large size of scattering elements, the passive beamforming is typically challenged by the high computational complexity and inexact channel information.
Toward Smart Security Enhancement of Federated Learning Networks
In this way, the EDs in FLNs can keep training data locally, which preserves privacy and reduces communication overheads.
Scalable and Communication-efficient Decentralized Federated Edge Learning with Multi-blockchain Framework
This framework can achieve scalable and flexible decentralized FEL by individually manage local model updates or model sharing records for performance isolation.
Cryptography and Security
Optimization-driven Hierarchical Learning Framework for Wireless Powered Backscatter-aided Relay Communications
In this paper, we employ multiple wireless-powered relays to assist information transmission from a multi-antenna access point to a single-antenna receiver.
Radio Resource Management in Joint Radar and Communication: A Comprehensive Survey
First, we give fundamental concepts of JRC, important performance metrics used in JRC systems, and applications of the JRC systems.
Joint Auction-Coalition Formation Framework for Communication-Efficient Federated Learning in UAV-Enabled Internet of Vehicles
The simulation results show that the grand coalition, where all UAVs join a single coalition, is not always stable due to the profit-maximizing behavior of the UAVs.
Networking and Internet Architecture Signal Processing
Cognitive Radio Network Throughput Maximization with Deep Reinforcement Learning
In this paper, deep reinforcement learning is proposed to overcome the mentioned shortcomings and allow a wireless gateway to derive an optimal policy to maximize network throughput.
Predictive Maintenance for Edge-Based Sensor Networks: A Deep Reinforcement Learning Approach
In this paper, a model-free Deep Reinforcement Learning algorithm is proposed for predictive equipment maintenance from an equipment-based sensor network context.
Federated Learning for 6G Communications: Challenges, Methods, and Future Directions
As the 5G communication networks are being widely deployed worldwide, both industry and academia have started to move beyond 5G and explore 6G communications.
Incentive Mechanism Design for Resource Sharing in Collaborative Edge Learning
In 5G and Beyond networks, Artificial Intelligence applications are expected to be increasingly ubiquitous.
Optimization-driven Deep Reinforcement Learning for Robust Beamforming in IRS-assisted Wireless Communications
Intelligent reflecting surface (IRS) is a promising technology to assist downlink information transmissions from a multi-antenna access point (AP) to a receiver.
Robust Beamforming for IRS-assisted Wireless Communications under Channel Uncertainty
By adjusting the magnitude of reflecting coefficients, the IRS can sustain its operations by harvesting energy from the AP's signal beamforming.
A Secure Federated Learning Framework for 5G Networks
In this article, we propose a blockchain-based secure FL framework to create smart contracts and prevent malicious or unreliable participants from involving in FL.
Demand-Side Scheduling Based on Deep Actor-Critic Learning for Smart Grids
While previous works have introduced centralised approaches, we formulate the smart grid environment as a Markov game, where each household is a decentralised agent, and the grid operator produces a price signal that adapts to the energy demand.
Local Differential Privacy based Federated Learning for Internet of Things
To avoid the privacy threat and reduce the communication cost, in this paper, we propose to integrate federated learning and local differential privacy (LDP) to facilitate the crowdsourcing applications to achieve the machine learning model.
Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach
Coupled with the rise of Deep Learning, the wealth of data and enhanced computation capabilities of Internet of Vehicles (IoV) components enable effective Artificial Intelligence (AI) based models to be built.
Signal Processing Networking and Internet Architecture
Resource Management for Blockchain-enabled Federated Learning: A Deep Reinforcement Learning Approach
However, the issue of BFL is that the mobile devices have energy and CPU constraints that may reduce the system lifetime and training efficiency.
Decentralized Learning for Channel Allocation in IoT Networks over Unlicensed Bandwidth as a Contextual Multi-player Multi-armed Bandit Game
We study a decentralized channel allocation problem in an ad-hoc Internet of Things network underlaying on the spectrum licensed to a primary cellular network.
Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach
Through extensive case studies on a real-world dataset, it is shown that FedGRU's prediction accuracy is 90. 96% higher than the advanced deep learning models, which confirm that FedGRU can achieve accurate and timely traffic prediction without compromising the privacy and security of raw data.
A Generative Learning Approach for Spatio-temporal Modeling in Connected Vehicular Network
Spatio-temporal modeling of wireless access latency is of great importance for connected-vehicular systems.
Deep Reinforcement Learning Based Intelligent Reflecting Surface for Secure Wireless Communications
As the system is highly dynamic and complex, and it is challenging to address the non-convex optimization problem, a novel deep reinforcement learning (DRL)-based secure beamforming approach is firstly proposed to achieve the optimal beamforming policy against eavesdroppers in dynamic environments.
Optimal Pricing of Internet of Things: A Machine Learning Approach
For service bundles, the subscription fee and data sizes of the grouped IoT services are optimized to maximize the total profit of cooperative service providers.
Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning
Deep Reinforcement Learning (DRL) has numerous applications in the real world thanks to its outstanding ability in quickly adapting to the surrounding environments.
Toward an Automated Auction Framework for Wireless Federated Learning Services Market
In this paper, we propose an auction based market model for incentivizing data owners to participate in federated learning.
Computer Science and Game Theory
Reconfigurable Intelligent Surface Aided Power Control for Physical-Layer Broadcasting
Our goalis to minimize the transmit power at the BS by jointly designing the transmit beamforming at the BSand the phase shifts of the passive elements at the RIS.
Blockchain for Future Smart Grid: A Comprehensive Survey
In this paper, we aim to provide a comprehensive survey on application of blockchain in smart grid.
Cryptography and Security Distributed, Parallel, and Cluster Computing Networking and Internet Architecture Social and Information Networks Systems and Control Systems and Control
Resource Allocation in Mobility-Aware Federated Learning Networks: A Deep Reinforcement Learning Approach
However, federated learning faces the energy constraints of the workers and the high network resource cost due to the fact that a number of global model transmissions may be required to achieve the target accuracy.
Reliable Federated Learning for Mobile Networks
Based on this metric, a reliable worker selection scheme is proposed for federated learning tasks.
Cryptography and Security
Re-route Package Pickup and Delivery Planning with Random Demands
Recently, a higher competition in logistics business introduces new challenges to the vehicle routing problem (VRP).
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.
Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices
To help manufacturers develop a smart home system, we design a federated learning (FL) system leveraging the reputation mechanism to assist home appliance manufacturers to train a machine learning model based on customers' data.
Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach
To strengthen data privacy and security, federated learning as an emerging machine learning technique is proposed to enable large-scale nodes, e. g., mobile devices, to distributedly train and globally share models without revealing their local data.
Distributed Learning for Channel Allocation Over a Shared Spectrum
When the CSI is time varying and unknown to the users, the users face the challenge of both learning the channel statistics online and converge to a good channel allocation.
Efficient Training Management for Mobile Crowd-Machine Learning: A Deep Reinforcement Learning Approach
In this letter, we propose to adopt a deep- Q learning algorithm that allows the server to learn and find optimal decisions without any a priori knowledge of network dynamics.
Networking and Internet Architecture
Joint Service Pricing and Cooperative Relay Communication for Federated Learning
However, the learning process of the existing federated learning platforms rely on the direct communication between the model owner, e. g., central cloud or edge server, and the mobile devices for transferring the model update.
Cryptography and Security Computer Science and Game Theory
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e. g., decisions or actions, given their states when the state and action spaces are small.
Deep Reinforcement Learning for Time Scheduling in RF-Powered Backscatter Cognitive Radio Networks
To coordinate the transmission of multiple secondary transmitters, the secondary gateway needs to schedule the backscattering time, energy harvesting time, and transmission time among them.
Optimal and Low-Complexity Dynamic Spectrum Access for RF-Powered Ambient Backscatter System with Online Reinforcement Learning
To cope with such incomplete knowledge of the environment, we develop a low-complexity online reinforcement learning algorithm that allows the secondary transmitter to "learn" from its decisions and then attain the optimal policy.
Joint Ground and Aerial Package Delivery Services: A Stochastic Optimization Approach
A considerable number of works have studied different aspects of drone package delivery service by a supplier, one of which is delivery planning.
A Survey on Consensus Mechanisms and Mining Strategy Management in Blockchain Networks
This survey is motivated by the lack of a comprehensive literature review on the development of decentralized consensus mechanisms in blockchain networks.
Cryptography and Security
Optimal Stochastic Package Delivery Planning with Deadline: A Cardinality Minimization in Routing
Vehicle Routing Problem with Private fleet and common Carrier (VRPPC) has been proposed to help a supplier manage package delivery services from a single depot to multiple customers.
Optimal Stochastic Delivery Planning in Full-Truckload and Less-Than-Truckload Delivery
In reality, some parameters are random and unknown.
Decentralized Caching for Content Delivery Based on Blockchain: A Game Theoretic Perspective
In this paper, we propose a decentralized framework of proactive caching in a hierarchical wireless network based on blockchains.
Networking and Internet Architecture
Cyberattack Detection in Mobile Cloud Computing: A Deep Learning Approach
With the rapid growth of mobile applications and cloud computing, mobile cloud computing has attracted great interest from both academia and industry.
Optimal Auction For Edge Computing Resource Management in Mobile Blockchain Networks: A Deep Learning Approach
However, a mechanism needs to be designed for edge resource allocation to maximize the revenue for the Edge Computing Service Provider and to ensure incentive compatibility and individual rationality is still open.
Computer Science and Game Theory
Mobile Big Data Analytics Using Deep Learning and Apache Spark
The proliferation of mobile devices, such as smartphones and Internet of Things (IoT) gadgets, results in the recent mobile big data (MBD) era.
Deep Activity Recognition Models with Triaxial Accelerometers
Despite the widespread installation of accelerometers in almost all mobile phones and wearable devices, activity recognition using accelerometers is still immature due to the poor recognition accuracy of existing recognition methods and the scarcity of labeled training data.
Toward a Robust Sparse Data Representation for Wireless Sensor Networks
Our contributions that address three major issues include: 1) an effective method that extracts population sparsity of the data, 2) a sparsity ratio guarantee scheme, and 3) a customized learning algorithm of the sparsifying dictionary.
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs).
