Multi-task Domain Adaptation for Computation Offloading in Edge-intelligence Networks
School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；School of Cyber Engineering, Xidian University（西安电子科技大学网络与信息安全学院）；National and Kapodistrian University of Athens（雅典国立卡波迪斯特里亚大学）；University of Illinois Chicago（芝加哥大学伊利诺伊分校）；School of Electrical and Electronics Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）
Runxin Han, Bo Yang, Zhiwen Yu, Xuelin Cao, George C. Alexandropoulos, Chau Yuen
In the field of multi-access edge computing (MEC), efficient computation offloading is crucial for improving resource utilization and reducing latency in dynamically changing environments. This paper introduces a new approach, termed as Multi-Task Domain Adaptation (MTDA), aiming to enhance the ability of computational offloading models to generalize in the presence of domain shifts, i.e., when new data in the target environment significantly differs from the data in the source domain. The proposed MTDA model incorporates a teacher-student architecture that allows continuous adaptation without necessitating access to the source domain data during inference, thereby maintaining privacy and reducing computational overhead. Utilizing a multi-task learning framework that simultaneously manages offloading decisions and resource allocation, the proposed MTDA approach outperforms benchmark methods regarding mean squared error and accuracy, particularly in environments with increasing numbers of users. It is observed by means of computer simulation that the proposed MTDA model maintains high performance across various scenarios, demonstrating its potential for practical deployment in emerging MEC applications.
Edge Intelligence for Energy-efficient Computation Offloading and Resource Allocation in 5G Beyond
University of Electronic Science and Technology of China（电子科技大学）；University of Oslo（奥斯陆大学）；Simula Metropolitan Center for Digital Engineering（Simula大都会数字工程中心）
TwinLoop: Simulation-in-the-Loop Digital Twins for Online Multi-Agent Reinforcement Learning
Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology（南方科技大学可信自主系统研究院）；Department of Computer Science and Engineering, Southern University of Science and Technology（南方科技大学计算机科学与工程系）；Department of Informatics and Telecommunications, University of Thessaly（色萨利大学信息与电信系）
CASCADE: Case-Based Continual Adaptation for Large Language Models During Deployment
School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, Jilin University（吉林大学知识驱动人机智能工程研究中心）；International Center of Future Science, Jilin University（吉林大学未来科学国际中心）；Department of Informatics, King’s College London（伦敦国王学院信息学院）；The Alan Turing Institute（艾伦·图灵研究所）；AI Centre, Department of Computer Science, UCL（UCL计算机科学系人工智能中心）
Evaluating Artificial Intelligence Algorithms for the Standardization of Transtibial Prosthetic Socket Shape Design
D Lab, Radboud University Medical Centre（radboud大学医学中心3D实验室）；Department of Trauma Surgery, Radboud University Medical Centre（radboud大学医学中心创伤外科部门）；Department of Rehabilitation, Radboud University Medical Centre（radboud大学医学中心康复部门）；Donders Centre for Cognition, Radboud University（radboud大学认知中心）；Military Health Organisation, Ministry of Defence, Kromhout Kazerne（国防部军事医疗组织，克罗姆霍特凯尔内）
