Proposed in 2016 as a privacy enhancing technique, federated learning and analytics (FL & FA) made remarkable progress in theory and practice in recent years. However, there is a growing disconnect between theoretical research and practical applications of federated learning. This workshop aims to bring academics and practitioners closer together to exchange ideas: discuss actual systems and practical applications to inspire researchers to work on theoretical and practical research questions that lead to real-world impact; understand the current development and highlight future directions. To achieve this goal, we aim to have a set of keynote talks and panelists by industry researchers focused on deploying federated learning and analytics in practice, and academic research leaders who are interested in bridging the gap between the theory and practice.

Topics of interest include, but are not limited to, the following:
Scalable and robust federated machine learning systems.
Novel cross-device and cross-silo production applications.
Training, fine-tuning, and personalizing (foundation) models in federated settings.
Federated analytics vs. federated learning: synergies and differences in algorithms and systems (characteristics, constraints and orchestration).
Approaches for addressing distribution shifts and continual learning in federated settings.
Autotuned federated algorithms for hyperparameters, model architectures etc.
Federated learning and analytics as part of an AI lifecycle.
Open-source frameworks and community for federated learning and analytics.
Theoretical studies with realistic assumptions for practical settings.
Differential privacy and other privacy-preserving technologies in federated settings.
Privacy attacks and empirical privacy auditing techniques in federated contexts.
Security attacks and defenses in federated settings.
Multi-party computation protocols & trusted execution environments for federated computations.
Challenges in fully decentralized networks compared to federated settings.
Trustworthy decentralized learning at scale.
Fairness and responsible models in federated settings.
Social impact and privacy policies in federated settings


Submission Due Date: May 22nd, 2023, AoE
Notification of Acceptance: June 19th, 2023, AoE
Workshop Dates: Friday, July 28th, 2023, Hawaii


Submission Instructions
Submissions should be double-blind, no more than 4 pages long (excluding references), and following the ICML'23 template. An optional appendix of any length can be put at the end of the draft (after references).

Submissions are processed in OpenReview: https://openreview.net/group?id=ICML.cc/2023/Workshop/FL. Our workshop does not have formal proceedings, i.e., it is non-archival. Accepted papers and their review comments will be posted on OpenReview in public (after the end of the review process), while rejected and withdrawn papers and their reviews will remain private.

Presentation Instructions
All accepted papers are expected to be presented in person. The workshop will not provide support for virtual talks or posters.