IoTCIT 2022 Workshop 4尚智物联诚邀投稿---欢迎参加2022物联网、通信与智能技术国际会议Workshop4!
来源: 吴贺俊/


物联网,通信与智能技术国际会议 (IoTCIT 2022)将于2022年4月份(具体日期待定)在中国长沙召开,诚挚邀请大家投稿到:IoTCIT 2022 Workshop 4: Distributed Learning for Smart and Practical IoT。第一轮截稿日期为2022年1月1日。请根据大会提供的模板准备稿件(模板链接: 。建议8页以内,超页将收取Extra Page Charge),请将投稿邮件的主题命名为“paper title-workshop 4”,然后将稿件发送到: . 高质量的录用稿件将被推荐到SCI 期刊发表。

WORKSHOP 4:智能物联网的分布式学习


关键词: 联邦学习,群体学习,区块链,多智能体强化学习,物联网




  1. 联邦学习,不需要感知数据共享中心,而是在物联网中以分布式方式训练的机器学习方式;
  2. 群体学习,即不需要中央协调,将边缘计算、基于区块链的点对点网络结合在一起的群体学习方法;
  3. 多智能体强化学习,可应用于物联网充电和移动控制或通信、资源分配、任务调度等决策的多智能体强化学习方案。



中山大学计算机学院、人工智能学院副教授。主要研究方向为智能物联网(AIoT),自主移动机器人集群,分布式智能感知,参与了国家自然科学基金重大项目和国家科技计划重点研发项目。近年在顶级国际会议和期刊包括IEEE IOT、TPDS、TWC、TKDE、TCSVT、ACM TWEB、INFOCOM等发表论文40余篇,曾获IEEE WCNC最佳论文奖,ISSNIP 最佳论文奖。

Workshop 4: Distributed Learning for Smart and Practical IoT

Title: Need for Intelligence: Learning in the Internet of Things

Keywords: Federated Learning, Swarm Learning, Blockchain, Multi-agent Reinforcement Learning, IoT

Summary: Internet of Things (IoT) and machine learning are two important techniques in most industrial, business, agricultural, and medical applications. On the one hand, IoT systems keep producing massive sensory data as the input of various services. On the other hand, machine learning has obtained great success in vision, graphics, natural language processing, gaming, and controlling. This workshop calls for works demonstrating the most recent progress and contributions to learning in IoT. In particular, this workshop will focus on the follows (1) In-network federated learning, which does not need a center for sensory data sharing, but trains the machine learning model in a distributed fashion within the IoT; (2) Swarm learning that unites edge computing, blockchain-based peer-to-peer networking, without the need for a central coordinator. (3) Multi-agent reinforcement learning schemes for control of charging and moving, or decision making of communication, resource allocation, task scheduling, etc. This workshop especially encourages applications of learning techniques that make battery charging, event detection, localization in IoTs practical. Please name the email title of the submission with “paper title_workshop title”, when sending an email to this workshop.

Chair: Dr. Hejun WuSun Yat-sen University

Hejun Wu works as an associate professor at the School of Computer Science and Engineering. He is also with the School of Artificial Intelligence, Sun Yat-Sen University. His main research interests are Artificial Intelligent Internet of Things (AIoT) and Mobile Internet of Things (MIoT), clusters of autonomous mobile robots, and distributed parallel perception. He was the principal investigator of projects granted from the General Program of the National Natural Science Foundation of China. Besides, he participated in the Major Research Plan of the National Natural Science Foundation of China and the key project of the National Programs for Science and Technology Development. Moreover, he has published more than 40 papers on top international conferences and journals in recent years including IEEE IoT, TPDS, TWC, TKDE, TCSVT, ACM TWEB, INFOCOM, etc. He won the IEEE WCNC Best Paper Award and ISSNIP Best Paper Award.

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