In recent years, thanks to technological breakthroughs in algorithms, computing power, and data analysis, industrial internet is undergoing a shift from digitization and networking to intelligence. As the intelligence of industrial devices continues to i ncrease, their trustworthy including trust, security and privacy, reliability and resilience is being incre asingly considered, for instances, vulnerability assessment, APT attacks, zero-trust access, the security of the m achine learning model itself, and among others in computing and communications. Through the fundamental differences be tween industrial internet and the regular IT systems, their common vulnerabilities and priorities are different. Further more, industrial internet has a specific type of traffic and data using particular communication protocols. Machine learning-b ased security solutions have been widely applied to the detection of threats in industrial internet systems. Reinforcement learning (e.g., deep Q-networks, continuous control with deep reinforcement learning, distributional deep reinforcement learning with quantile regression, etc.) and federated learning are two representative methods to analyze behaviors and patterns of malicious activities, and to make decisions accordingly. While they are rapidly expanding in the field of trustworthy in industrial internet, several research issues are still open to be discussed and studied, from high requirements on real time and fault tolerance, resource-constrained industrial internet devices for building ML-based prediction models, large noise in industrial internet data, to multiple intermediate processes for computing and communications. The objective of this workshop is to identify and address the challenges and opportunities of developing and utilizing reinforcement learning and federated learning in the area of trustworthy in industrial internet. Each submission must be up to 5 pages in length and conforms to the double-column template provided by IEEE. Potential topics include but are not limited to the following:

  • New Reinforcement learning method for trustworthy in industrial internet
  • New Federated learning method for trustworthy in industrial internet
  • Resource allocation for Reinforcement learning and Federated learning for trustworthy in industrial internet
  • Incentive mechanisms of Reinforcement learning and Federated learning for trustworthy in industrial internet
  • Lightweight Reinforcement learning and Federated learning for trustworthy in industrial internet
  • Scalable Reinforcement learning and Federated learning for trustworthy in industrial internet
  • Architectures for Reinforcement learning and Federated learning for trustworthy in industrial internet
  • Dataset for Reinforcement learning and Federated learning for trustworthy in industrial internet
  • Open-source tool for Reinforcement learning and Federated learning for trustworthy in industrial internet
  • Implementation and prototype of reinforcement learning methods for trustworthy in industrial internet
  • Implementation and prototype of federated learning methods for trustworthy in industrial internet
  • New applications of Reinforcement learning and Federated learning for trustworthy in industrial internet
  • Software infrastructure for Reinforcement learning for trustworthy in industrial internet
  • Software infrastructure Middleware development for Federated learning enabled trustworthy in industrial internet
  • Implementation and prototype of reinforcement learning methods for trustworthy in industrial internet
  • Implementation and prototype of federated learning methods for trustworthy in industrial internet
  • Paper Submission Guideline:

    This workshop is in conjunction with IEEE TrustCom 2021 (https://trustcom2021.sau.edu.cn/) and all papers must be submitted electronically via Easychair (https://easychair.org/conferences/?conf=rfti2021 ). Distinguished papers selected from RFTI 2021, after further extensions, will be recommended for submission and publication in Special Issues of SCI-indexed Journals.

    Important dates:

  • Submission deadline: 25 April 2021
  • Workshop paper acceptance: 05 July 2021
  • Final paper submission: 10 July 2021
  • Workshop Chairs

  • Wei Zhao (Anhui University of Technology, China. zhaoweistuart@gmail.com)
  • Weifeng Sun (Dalian University of Technology, China. wfsun@dlut.edu.cn)
  • Bo Zhang (Zhengzhou University , China. zhangbo2050@zzu.edu.cn)
  • Hanqing Wu (The Hong Kong Polytechnic University, Hong Kong, China. cshwu@comp.polyu.edu.hk)
  • TPC Chairs:

  • Xun Shao (Kitami Institute of Technology, Japan. x-shao@ieee.org)
  • Xuangou Wu (Anhui University of Technology, China. wuxgou@ahut.edu.cn)
  • Web Chair:

  • Chen Xu (Anhui University of Technology, stackcapo@163.com)