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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.
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.
Dynamic Federated Learning-Based Economic Framework for Internet-of-Vehicles
Federated learning (FL) can empower Internet-of-Vehicles (IoV) networks by leveraging smart vehicles (SVs) to participate in the learning process with minimum data exchanges and privacy disclosure.
Federated Learning Meets Contract Theory: Energy-Efficient Framework for Electric Vehicle Networks
In this paper, we propose a novel energy-efficient framework for an electric vehicle (EV) network using a contract theoretic-based economic model to maximize the profits of charging stations (CSs) and improve the social welfare of the network.
Networking and Internet Architecture Signal Processing
Energy Demand Prediction with Federated Learning for Electric Vehicle Networks
Through experimental results, we show that our proposed approaches can improve the accuracy of energy demand prediction up to 24. 63% and decrease communication overhead by 83. 4% compared with other baseline machine learning algorithms.
