FL @ ICML 2023

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:

Federated Learning and Analytics:

  • 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.

Privacy and Security in Federated 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.

Decentralized Networks and Trustworthiness:

  • Challenges in fully decentralized networks compared to federated settings.
  • Trustworthy decentralized learning at scale.

Fairness, Responsibility, and Social Impact:

  • Fairness and responsible models in federated settings.
  • Social impact and privacy policies in federated settings.


Call for Papers

Important Dates
  • Submission Due Date: May 22nd, May 28th, 2023, AoE
  • Notification of Acceptance: June 19th, 2023, AoE
  • Free Registration Application Due: June 21st, 2023, AoE
  • Camera-ready Papers Due: July 17th, 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.

We welcome sumbissions from novel research, ongoing (incomplete) projects, draft currently under review at other venues, as well as recently published results. However, we request significant updates if the work has previously been presented at major machine learning conferences or workshops before Jul. 1st, 2023, or has been presented at any conferences or workshops before Jan 1st, 2023.

Camera Ready Instructions

Feel free to use the template adapted from the official ICML'23 latex paper template. The main change is the footnote about the workshop.

We allow an extra page (5 pages for main context) to incorporate reviewer feedback, and adding additional information such as authors formatting, limitations, ethics and acknowledgement.

Presentation Instructions

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

The posters should be portrait orientation and up to 24"w x 36"h size. Feel free to use the ICML poster printing service before their deadlines.



Best Paper Awards

Best student paper

Best student paper honorable mention

Early Career Free Registration

The workshop can provide limited number of free (full ICML'23 conference) registration to our attendees, which will prioritize early career students, and promote diversity, equity and inclusion (DEI). If you are interested, please email us at fl-workshop-icml23@googlegroups.com following the instructions:

  • Email has to be sent before June 21st to be considered.
  • Email title starts with [FL-ICML23 free registration].
  • Includes link(s) to your accepted, or submitted paper(s) to our workshop.
  • Includes a short paragraph describing why it is important for your research and career.
  • (Optional) includes link(s) to your webpage and resume.
  • The awardees will be announced in late June. Registration fee can be refunded if you already registered.

Congratulations to the following awardees, and thanks for your contribution to the workshop: Gaurav Bagwe (MTU), Misha Khodak (CMU), Tatsuki Koga (UCSD), Gwen Legate (Mila), Edward Nguyen (Rice), Tomas Ortega (UCI), Rishub Tamirisa (UIUC), Jiayi Wang (Utah), Yeojoon Youn(Gatech).

Best Reviewers Free Registration

The workshop encourages high quality reviews. We provide limited number of free (full ICML'23 conference) registration for self-nominated reviewers who have written high-quality reviews. If you are interested, please email us at fl-workshop-icml23@googlegroups.com following the instructions:

  • Email has to be sent before June 21st to be considered.
  • Email title starts with [FL-ICML23 free registration: reviewer].
  • Includes link(s), or screenshots to your reviews.
  • The awardees will be announced in late June. Registration fee can be refunded if you already registered.

Congratulations to the following awardees, and thanks for your contribution to the workshop: Ali Shahin Shamsabadi (Brave Software).


Workshop Program

The following program is Hawaii local time on Friday July 28th. The workshop is colocated with ICML at Hawaii Convention Center, Room 311.

Local Time (UTC-10) Activity
09:00AM - 09:05AM Introduction and Opening Remarks
09:05AM - 09:40AM Invited Talk: Vojta Jina
  • Lessons from Applying Private Federated Learning
09:40AM - 10:00AM

Two Spotlight Talks

10:00AM - 10:15AM Break
10:15AM - 10:50AM Invited Talk: Li Xiong
  • Federated Learning with Personalized and User-level Differential Privacy
10:50AM - 11:25AM Invited Talk: Brendan McMahan
  • Advances in Privacy and Federated Learning, with Applications to GBoard
11:25AM - 13:30PM Poster and Lunch
13:30PM - 14:25PM Panel Discussion: Salman Avestimehr, Kamalika Chaudhuri, Song Han, Florian Tramèr; and Peter Kairouz (moderator)
  • Privacy-Preserving & Trustworthy LLMs: Challenges, Opportunities, & the Role of FL
14:25PM - 15:00PM Invited Talk: Ce Zhang
  • Optimizing Communications and Data for Distributed and Decentralized Learning
15:00PM - 15:15PM Break
15:15PM - 15:50PM Invited Talk: Giulia Fanti
  • New Variants of Old Challenges in Data Valuation and Privacy
15:50PM - 16:20PM

Three Spotlight Talks

16:20PM - 16:55PM

Invited Talk: Chuan Guo

  • Towards (Truly) Private and Communication-efficient Federated Learning
16:55PM - 17:00PM Concluding Remarks

Accepted Papers

Accepted Papers

Spotlight Presentations
Poster Presentations


Invited Speakers

Ce Zhang


Li Xiong


Panel Discussion



Workshop Organizers

Peter Kairouz


Bo Li


Tian Li


Jianyu Wang


Ayfer Ozgur


Zheng Xu


Website Admin

Chulin Xie


Program Committee

  • Aditya Balu (Iowa State University)
  • Adrian Gascon (Google)
  • Ahmed M. Abdelmoniem (Queen Mary University of London)
  • Ali Anwar (University of Minnesota)
  • Ali Shahin Shamsabadi (Alan Turing Institute)
  • Alp Yurtsever (Umeå University)
  • Amir Houmansadr (University of Massachusetts Amherst)
  • Ananda Suresh (Google)
  • Andre Manoel (Microsoft)
  • Andreas Haeberlen (University of Pennsylvania)
  • Andrew Hard (Google)
  • Anshuman Suri (University of Virginia)
  • Antonious Girgis (University of California Los Angeles)
  • Aritra Mitra (North Carolina State University)
  • Arun Ganesh (Google)
  • Ashwinee Panda (Princeton University)
  • Aurélien Bellet (INRIA)
  • Berivan Isik (Stanford University)
  • Bing Luo (Duke University)
  • Cesar Uribe (Rice University)
  • Chao Ren (Nanyang Technological University)
  • Charlie Hou (Carnegie Mellon University)
  • Chuan Xu (INRIA)
  • Chuizheng Meng (University of Southern California)
  • Chulin Xie (University of Illinois Urbana Champaign)
  • Dan Alistarh (Institute of Science and Technology)
  • Daniel Beutel (University of Cambridge)
  • Deepesh Data (University of California Los Angeles)
  • Dimitrios Dimitriadis (Amazon)
  • Edwige Cyffers (INRIA)
  • Egor Shulgin (KAUST)
  • Evita Bakopoulou (Google)
  • Fan Lai (University of Michigan)
  • Fan Mo (Huawei Technologies Ltd.)
  • Fatemehsadat Mireshghallah (University of California San Diego)
  • Florian Tramèr (ETH Zurich)
  • Galen Andrew (Google)
  • Giulia Fanti (Carnegie Mellon University)
  • Giulio Zizzo (IBM)
  • Graham Cormode (Facebook)
  • Grigory Malinovsky (King Abdullah University of Science and Technology)
  • Haibo Yang (Ohio State University)
  • Hamed Haddadi (Imperial College London)
  • Hamed Hassani (University of Pennsylvania)
  • Han Yu (Nanyang Technological University)
  • Jalaj Upadhyay (Rutgers University)
  • James Bell (Google)
  • Jayadev Acharya (Cornell University)
  • Jayanth Regatti (Ohio State University)
  • Jiankai Sun (ByteDance Inc.)
  • Jiayi Wang (University of Utah)
  • Jiayuan Ye (National University of Singapore)
  • Jihong Park (Deakin University)
  • Jinghui Chen (Pennsylvania State University)
  • Jinhyun So (Samsung)
  • Jonas Geiping (University of Maryland College Park)
  • Jonathan Ullman (Northeastern University)
  • Kaan Ozkara (University of California Los Angeles)
  • Kai Yi (KAUST)
  • Kai Yue (North Carolina State University)
  • Kaiyuan Zhang (Purdue University)
  • Karthik Prasad (Facebook AI)
  • Ken Liu (Carnegie Mellon University)
  • Kevin Hsieh (Microsoft)
  • Konstantin Mishchenko (Samsung)
  • Konstantinos Psounis (University of Southern California)
  • Krishna Pillutla (Google)
  • Kumar Kshitij Patel (Toyota Technological Institute at Chicago)
  • Lalitha Sankar (Arizona State University)
  • Lie He (Swiss Federal Institute of Technology Lausanne)
  • Lun Wang (Google)
  • Lydia Zakynthinou (Northeastern University)
  • Marco Canini (KAUST)
  • Mathieu Even (INRIA)
  • Mathilde Jay (Université Grenoble Alpes)
  • Mi Zhang (The Ohio State University)
  • Michael Rabbat (McGill University)
  • Michal Yemini (Bar-Ilan University)
  • Mikko Heikkilä (University of Helsinki)
  • Milad Nasr (Google)
  • Mingrui Liu (George Mason University)
  • Mingyi Hong (University of Minnesota Minneapolis)
  • Mingzhe Chen (University of Miami)
  • Minhao Cheng (Hong Kong University of Science and Technology)
  • Mónica Ribero (Google)
  • Murali Annavaram (University of Southern California)
  • Nasimeh Heydaribeni (University of California San Diego)
  • Nicholas Lane (University of Cambridge)
  • Olga Ohrimenko (The University of Melbourne)
  • Paulo Ferreira (Dell Technologies)
  • Pengchao Han (The Chinese University of Hong Kong Shenzhen)
  • Peter Richtárik (KAUST)
  • Pranay Sharma (Carnegie Mellon University)
  • Praneeth Vepakomma (Massachusetts Institute of Technology)
  • Prashant Khanduri (Wayne State University)
  • Raed Al Kontar (University of Michigan)
  • Reese Pathak (University of California Berkeley)
  • Rudrajit Das (University of Texas Austin)
  • Sai Karimireddy (University of California Berkeley)
  • Salim El Rouayheb (Rutgers University)
  • Satoshi Hara (Osaka University)
  • Saurabh Bagchi (KeyByte LLC)
  • Se-Young Yun (KAIST)
  • Sebastian Stich (CISPA Helmholtz Center for Information Security)
  • Sewoong Oh (University of Washington)
  • Shangwei Guo (Chongqing University)
  • Songtao Lu (IBM Thomas J. Watson Research Center)
  • Songze Li (The Hong Kong University of Science and Technology)
  • Stefanos Laskaridis (Brave Software)
  • Swanand Kadhe (IBM)
  • Taha Toghani (Rice University)
  • Tahseen Rabbani (University of Maryland College Park)
  • Virendra Marathe (Oracle)
  • Vladimir Braverman (Rice University)
  • Walid Saad (Virginia Tech)
  • Wei-Ning Chen (Stanford University)
  • Yae Jee Cho (Carnegie Mellon University)
  • Yang Liu (Tsinghua University)
  • Yangsibo Huang (Princeton University)
  • Yaodong Yu (University of California Berkeley)
  • Yi Zhou (IBM)
  • Yibo Zhang (Stanford University)
  • Yingyan Celine Lin (Georgia Institute of Technology)
  • Yu-Xiang Wang (UC Santa Barbara)
  • Yuanhao Xiong (University of California Los Angeles)
  • Yue Tan (University of Technology Sydney)
  • Yuqing Zhu (UC Santa Barbara)
  • Zaid Harchaoui (University of Washington Seattle)
  • Zhanhong Jiang (Johnson Controls Inc.)
  • Zhaozhuo Xu (Rice University)



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Contact us

Email us at fl-workshop-icml23@googlegroups.com