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Incentive Mechanism Design for Joint Resource Allocation in Blockchain-based Federated Learning
Since each client has a limited amount of computing resources, the problem of allocating computing resources into training and mining needs to be carefully addressed.
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.
A Contract Theory based Incentive Mechanism for Federated Learning
It is challenging to design proper incentives for the FL clients due to the fact that the task is privately trained by the clients.
Joint Transmit Precoding and Reflect Beamforming Design for IRS-Assisted MIMO Cognitive Radio Systems
Cognitive radio (CR) is an effective solution to improve the spectral efficiency (SE) of wireless communications by allowing the secondary users (SUs) to share spectrum with primary users (PUs).
Information Theory Signal Processing 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.
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).
Cross-Layer Coordinated Attacks on Cyber-Physical Systems: A LQG Game Framework with Controlled Observations
On the other hand, the interactions between the attacker and the defender in the physical layer significantly impact the observation and jamming strategies.
Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Management
However, due to the privacy concerns of devices and limited computation or communication resource of UAVs, it is impractical to send raw data of devices to UAV servers for model training.
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
Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach
Third, to adapt the proposed framework to the timeliness of industrial anomaly detection, we propose a gradient compression mechanism based on Top-\textit{k} selection to improve communication efficiency.
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
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.
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
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.
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.
Reliable Federated Learning for Mobile Networks
Based on this metric, a reliable worker selection scheme is proposed for federated learning tasks.
Cryptography and Security
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.
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 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
