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Found 13 papers, 2 papers with code
Joint Communication and Computation Framework for Goal-Oriented Semantic Communication with Distortion Rate Resilience
Recent research efforts on semantic communication have mostly considered accuracy as a main problem for optimizing goal-oriented communication systems.
Wirelessly Powered Federated Learning Networks: Joint Power Transfer, Data Sensing, Model Training, and Resource Allocation
This work for the first time investigates a resource allocation problem in collaborative sensing-assisted sustainable FL (S2FL) networks with the goal of minimizing the total completion time.
Label driven Knowledge Distillation for Federated Learning with non-IID Data
Moreover, leveraging the advantages of hierarchical network design, we propose a new label-driven knowledge distillation (LKD) technique at the global server to address the second problem.
Resource Allocation for Compression-aided Federated Learning with High Distortion Rate
Based on the property of a FL model, we first determine the number of IoT devices participating in the FL process.
HCFL: A High Compression Approach for Communication-Efficient Federated Learning in Very Large Scale IoT Networks
Federated learning (FL) is a new artificial intelligence concept that enables Internet-of-Things (IoT) devices to learn a collaborative model without sending the raw data to centralized nodes for processing.
Federated Learning for Smart Healthcare: A Survey
Recent advances in communication technologies and Internet-of-Medical-Things have transformed smart healthcare enabled by artificial intelligence (AI).
Federated Learning Framework with Straggling Mitigation and Privacy-Awareness for AI-based Mobile Application Services
In this work, we propose a novel framework to address straggling and privacy issues for federated learning (FL)-based mobile application services, taking into account limited computing/communications resources at mobile users (MUs)/mobile application provider (MAP), privacy cost, the rationality and incentive competition among MUs in contributing data to the MAP.
UAV Communications for Sustainable Federated Learning
Finally, simulations illustrate the potential of our proposed UAV-SFL approach in providing a sustainable solution for FL-based wireless networks, and in reducing the UAV transmit power by 32. 95%, 63. 18%, and 78. 81% compared with the benchmarks.
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.
Deep Learning for Intelligent Demand Response and Smart Grids: A Comprehensive Survey
Electricity is one of the mandatory commodities for mankind today.
Sum-Rate Maximization for UAV-assisted Visible Light Communications using NOMA: Swarm Intelligence meets Machine Learning
As the integration of unmanned aerial vehicles (UAVs) into visible light communications (VLC) can offer many benefits for massive-connectivity applications and services in 5G and beyond, this work considers a UAV-assisted VLC using non-orthogonal multiple-access.
Intelligent Radio Signal Processing: A Survey
Intelligent signal processing for wireless communications is a vital task in modern wireless systems, but it faces new challenges because of network heterogeneity, diverse service requirements, a massive number of connections, and various radio characteristics.
Swarm Intelligence for Next-Generation Wireless Networks: Recent Advances and Applications
Due to the proliferation of smart devices and emerging applications, many next-generation technologies have been paid for the development of wireless networks.
