首页|Researchers at Beijing Jiaotong University Release New Data on Machine Learning (Matching Game for Multi-task Federated Learning In Internet of Vehicles)

Researchers at Beijing Jiaotong University Release New Data on Machine Learning (Matching Game for Multi-task Federated Learning In Internet of Vehicles)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Fresh data on Machine Learning are presented in a new report. According to news originating from Beijing, People's Republic of Ch ina, by NewsRx correspondents, research stated, "To overcome the inherent defect s of massive data uploading and processing in traditional machine learning, fede rated learning is emerged as a promising tool given that it enables to implement privacy-preserved distributed machine learning in Internet of Vehicles (IoV). H owever, the performance of federated learning suffers from several challenges, e specially ineffective execution of delay-sensitive tasks triggered simultaneousl y by moving vehicles."Financial support for this research came from Fundamental Research Funds for the Central Universities. Our news journalists obtained a quote from the research from Beijing Jiaotong Un iversity, "To minimize the total execution delay of multiple tasks, we propose a multi-task federated learning framework which improves task completion rate and enables each task to be completed in time. Moreover, we also aim to improve the network utility of the IoV. The algorithm of joint optimization algorithm is pr oposed to achieve a stable partition of vehicle coalitions based on the block co ordinate descent (BCD) method, the matching game-based coalition method, and gra dient projection method. The performance of the proposed multi-task federated le arning is evaluated through numerical simulations in terms of total latency, net work utility, and accuracy of federated learning tasks."

BeijingPeople's Republic of ChinaAsi aAlgorithmsCyborgsEmerging TechnologiesMachine LearningBeijing Jiaoton g University

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(MAY.27)