IoTCIT將于2022年4月盛大召開,，歡迎大家投稿到Workshop4,！

物聯(lián)網(wǎng),，通信與智能技術(shù)國際會議 (IoTCIT 2022)將于2022年4月份（具體日期待定）在中國長沙召開,，誠摯邀請大家投稿到：IoTCIT 2022 Workshop 4: Distributed Learning for Smart and Practical IoT,。第一輪截稿日期為2022年1月1日,。請根據(jù)大會提供的模板準(zhǔn)備稿件（模板鏈接：http://www.iotcit.org/committee/author/ ,。建議8頁以內(nèi),，超頁將收取Extra Page Charge），請將投稿郵件的主題命名為“paper title-workshop 4”,，然后將稿件發(fā)送到: [email protected] . 高質(zhì)量的錄用稿件將被推薦到SCI 期刊發(fā)表,。

WORKSHOP 4：智能物聯(lián)網(wǎng)的分布式學(xué)習(xí)

標(biāo)題：尚智物聯(lián)---物聯(lián)網(wǎng)中的學(xué)習(xí)

關(guān)鍵詞: 聯(lián)邦學(xué)習(xí)，群體學(xué)習(xí),，區(qū)塊鏈,，多智能體強化學(xué)習(xí)，物聯(lián)網(wǎng)

簡介:

物聯(lián)網(wǎng)(IoT)和機器學(xué)習(xí)是大多數(shù)工業(yè),、商業(yè),、農(nóng)業(yè)和醫(yī)療應(yīng)用中的需要的兩項重要關(guān)鍵技術(shù)。一方面,，物聯(lián)網(wǎng)系統(tǒng)不斷產(chǎn)生大量的感知數(shù)據(jù),，作為各種服務(wù)的輸入；另一方面,，機器學(xué)習(xí)在視覺,、圖形、自然語言處理,、游戲和控制方面取得了巨大的成功,。本次會議將展示物聯(lián)網(wǎng)學(xué)習(xí)的最新進(jìn)展和貢獻(xiàn),。

本會將重點關(guān)注在物聯(lián)網(wǎng)內(nèi)應(yīng)用的以下內(nèi)容:

1. 聯(lián)邦學(xué)習(xí)，不需要感知數(shù)據(jù)共享中心,，而是在物聯(lián)網(wǎng)中以分布式方式訓(xùn)練的機器學(xué)習(xí)方式,；
2. 群體學(xué)習(xí)，即不需要中央?yún)f(xié)調(diào),，將邊緣計算,、基于區(qū)塊鏈的點對點網(wǎng)絡(luò)結(jié)合在一起的群體學(xué)習(xí)方法；
3. 多智能體強化學(xué)習(xí),，可應(yīng)用于物聯(lián)網(wǎng)充電和移動控制或通信,、資源分配、任務(wù)調(diào)度等決策的多智能體強化學(xué)習(xí)方案,。

特別鼓勵學(xué)習(xí)技術(shù)的應(yīng)用,，如電池充電，事件檢測,，定位在物聯(lián)網(wǎng)的實際應(yīng)用,。

主持人：吳賀俊（中山大學(xué)）

中山大學(xué)計算機學(xué)院,、人工智能學(xué)院副教授,。主要研究方向為智能物聯(lián)網(wǎng)(AIoT)，自主移動機器人集群,，分布式智能感知,，參與了國家自然科學(xué)基金重大項目和國家科技計劃重點研發(fā)項目。近年在頂級國際會議和期刊包括IEEE IOT,、TPDS,、TWC、TKDE,、TCSVT,、ACM TWEB、INFOCOM等發(fā)表論文40余篇,，曾獲IEEE WCNC最佳論文獎,，ISSNIP 最佳論文獎。

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.