In order to explore how the AI research community can adapt to this new regulatory reality, we propose the IEEE Intelligent Systems Special Issue on Federated Machine Learning. The special issue aims to stimulate discussions around open problems for privacy protection in machine learning and sharing of the most recent and ground-breaking work on the study and application of GDPR compliant machine learning. Technical issues include but not limit to data collection, integration, training and modelling, both in the centralized and distributed setting. Both theoretical and application-based contributions are welcome.

Selected papers from the FML 2019 workshop have already received invitations to be extended and submitted to this journal special issue in the acceptance notification emails.

Important Dates

Guest Editors

  • Qiang Yang, Webank and HKUST, Hong Kong
  • Yang Liu, Webank, China
  • Boi Faltings, EPFL, Switzerland
  • Fausto Giunchiglia, University of Trento, Italy
  • Han Yu, Nanyang Technological University, Singapore

Submission Guidelines

Submitted papers must conform to IEEE publication standard, with a word limit of submissions between 3,000 and 5,400 words (counting a standard figure or table as 200 words) and should follow IEEE Intelligent Systems style and presentation guidelines (www.computer.org/intelligent/author). All submissions will be peer-reviewed following standard journal practices. The manuscripts cannot have been published or be currently submitted for publication elsewhere. We strongly encourage submissions that include audio, video, and community content, which will be featured on the IEEE Computer Society website along with the accepted papers.

Please upload your article to: https://mc.manuscriptcentral.com/is-cs (log in and then select “Special Issue on Federated Machine Learning”).

If you have any questions about submitting your article, please contact the peer review coordinator at isystems@computer.org