Call for Papers [CFP in PDF]

Important Dates

Submission Due: Oct. 12 , 2020
Notification: Nov. 15 Nov. 20, 2020
Camera-ready: Nov. 25 Dec. 2, 2020



In the recent decade, we have witnessed rapid progress in machine learning in general and deep learning in particular, mostly driven by tremendous data. As these intelligent algorithms, systems, and applications are deployed in real-world scenarios, we are now facing new challenges, such as scalability, security, privacy, trust, cost, regulation, and environmental and societal impacts. In the meantime, data privacy and ownership has become more and more critical in many domains, such as finance, health, government, and social networks. Federated learning (FL) has emerged to address data privacy issues. To make FL practically scalable, useful, efficient, and effective on security and privacy mechanisms and policies, it calls for joint efforts from the community, academia, and industry. More challenges, interplays, and tradeoffs in scalability, privacy, and security need to be investigated in a more holistic and comprehensive manner by the community. We are expecting broader, deeper, and greater evolution of these concepts and technologies, and confluence towards holistic trustworthy AI ecosystems.

This workshop provides an open forum for researchers, practitioners, and system builders to exchange ideas, discuss, and shape roadmaps towards scalable and privacy-preserving federated learning in particular, and scalable and trustworthy AI ecosystems in general.


Topics of Interest

  • System scalability, reliability, and robustness in FL
  • Data, model, and knowledge scalability, compression, distillation in FL
  • Data, model, and knowledge privacy in FL
  • Data, network, knowledge, and system security in FL
  • Trustworthy assessment, audit, and verification in FL
  • Holistic design and resource management of FL algorithms and systems
  • Secure multi-party computation, learning, and reasoning
  • Scalability, privacy, and security in knowledge federation
  • Use cases and practices in real-world applications
  • Theoretical and economic analysis of FL systems
  • Attacks and defenses mechanisms and policies
  • Valuation, reward, and penalty algorithms, assessment, arbitration, and regulations
  • Scalable and trustworthy AI ecosystems
  • General federated learning and privacy-preserving distributed machine/deep learning


Submission Guidelines

Submissions can be up to 6 pages (excluding references). All accepted papers will be presented as posters; some may be selected for highlights or contributed talks, depending on schedule constraints. Accepted papers will be posted on the workshop website.

Please note that papers submissions should be anonymous, and will undergo double-blind peer review. Please follow the guidelines in the NeurIPS 2020 LaTeX style file. The final submission must be in PDF. Please submit your papers using EasyChair,


The workshop proceedings will be published on the workshop website, but are considered non-archival for the purposes of dual submissions. We welcome work that is under submission to a conference (please mention it in the appendix), and publishing at the workshop should not preclude you from submitting to conferences in the future. However, please check any conference policies as well.

Any questions may be directed to the workshop’s email address:

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