no code implementations • 17 Jun 2021 • Yuris Mulya Saputra, Diep N. Nguyen, Dinh Thai Hoang, Quoc-Viet Pham, Eryk Dutkiewicz, Won-Joo Hwang
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.
no code implementations • 15 Feb 2021 • Cong T. Nguyen, Nguyen Van Huynh, Nam H. Chu, Yuris Mulya Saputra, Dinh Thai Hoang, Diep N. Nguyen, Quoc-Viet Pham, Dusit Niyato, Eryk Dutkiewicz, Won-Joo Hwang
Finally, we highlight important challenges, open issues, and future research directions of TL in future wireless networks.
no code implementations • 1 Jan 2021 • Yuris Mulya Saputra, Dinh Thai Hoang, Diep N. Nguyen, Le-Nam Tran, Shimin Gong, Eryk Dutkiewicz
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.
no code implementations • 4 Apr 2020 • Yuris Mulya Saputra, Diep N. Nguyen, Dinh Thai Hoang, Thang Xuan Vu, Eryk Dutkiewicz, Symeon Chatzinotas
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
no code implementations • 3 Sep 2019 • Yuris Mulya Saputra, Dinh Thai Hoang, Diep N. Nguyen, Eryk Dutkiewicz, Markus Dominik Mueck, Srikathyayani Srikanteswara
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.