International Workshop on Federated Learning for User Privacy and Data Confidentiality
in Conjunction with ICML 2020 (FL-ICML'20)


Submission Due: May 17, 2020 June 8, 2020 (23:59 UTC-12)
Notification Due: May 31, 2020 June 30, 2020 (23:59 UTC-12)

Workshop Date: July 17-18, 2020
Venue: Virtual Workshop

Call for Papers

Training machine learning models in a centralized fashion often faces significant challenges due to regulatory and privacy concerns in real-world use cases. These include distributed training data, computational resources to create and maintain a central data repository, and regulatory guidelines (GDPR, HIPAA) that restrict sharing sensitive data. Federated learning (FL) is a new paradigm in machine learning that can mitigate these challenges by training a global model using distributed data, without the need for data sharing. The extensive application of machine learning to analyze and draw insight from real-world, distributed, and sensitive data necessitates familiarization with and adoption of this relevant and timely topic among the scientific community.

Despite the advantages of federated learning, and its successful application in certain industry-based cases, this field is still in its infancy due to new challenges that are imposed by limited visibility of the training data, potential lack of trust among participants training a single model, potential privacy inferences, and in some cases, limited or unreliable connectivity.

The goal of this workshop is to bring together researchers and practitioners interested in FL. This day-long event will facilitate interaction among students, scholars, and industry professionals from around the world to understand the topic, identify technical challenges, and discuss potential solutions. This will lead to an overall advancement of FL and its impact in the community. Topics of interest include, but are not limited to, the following:

  • Adversarial attacks on FL
  • Blockchain for FL
  • Fairness in FL
  • Hardware for on-device FL
  • Novel applications of FL
  • Operational challenges in FL
  • Personalization in FL
  • Privacy concerns in FL
  • Privacy-preserving methods for FL
  • Resource-efficient FL
  • System and infrastructure for FL
  • Theoretical contributions to FL
  • Uncertainty in FL

Proceedings and Dual Submission Policy

Our workshop has no formal proceedings. Accepted papers will be posted on the workshop webpage. We welcome submissions of unpublished papers, including those that are submitted/accepted to other venues if that other venue allows so. We will not accept papers that are already published though, because the goal of the workshop is to share recent results and discuss open problems.

Submission Instructions

Submissions must be at most 6 pages long, excluding references, and follow ICML-20 template. Submissions are single-blind and author identity will be revealed to the reviewers. An optional appendix of arbitrary length is allowed and should be put at the end of the paper (after references).

Easychair submission link: https://easychair.org/conferences/?conf=flicml20

If you have any enquiries, please email us at: flworkshop.icml.2020@gmail.com

Organizing Committee

  • Nathalie Baracaldo (IBM Research Almaden, USA)
  • Olivia Choudhury (Amazon, USA)
  • Gauri Joshi (Carnegie Mellon University, USA)
  • Ramesh Raskar (MIT Media Lab, USA)
  • Shiqiang Wang (IBM T. J. Watson Research Center, USA)
  • Han Yu (Nanyang Technological University, Singapore)

Program Committee

  • M. Hadi Amini (Florida International University, USA)
  • Mehdi Bennis (University of Oulu, Finland)
  • Supriyo Chakraborty (IBM Research, USA)
  • Boi Faltings (Ecole Polytechnique Fédérale de Lausanne, Switzerland)
  • Chaoyang He (University of Southern California, USA)
  • Mingyi Hong (University of Minnesota, USA)
  • Gauri Joshi (Carnegie Mellon University, USA)
  • Jakub Konečný (Google, USA)
  • Kin K. Leung (Imperial College, UK)
  • Dianbo Liu (Massachusetts Institute of Technology, USA)
  • Ji Liu (Stony Brook University, USA)
  • Yang Liu (Webank, China)
  • Jihong Park (Deakin University, Australia)
  • Peter Richtarik (King Abdullah University of Science and Technology, Saudi Arabia)
  • Andrew Trask (DeepMind, USA)
  • Lingfei Wu (IBM Research AI, USA)
  • Poonam Yadav (University of York, UK)
  • Mikhail Yurochkin (IBM Research, USA)

Organized by