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 FL, 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, while noting that FL has become an increasingly popular topic in the ICML community in recent years.

Topics of interest include, but are not limited to, the following:
- Adversarial attacks on FL
- Applications of FL
- Blockchain for FL
- Beyond first-order methods in FL
- Beyond local methods in FL
- Communication compression in FL
- Data heterogeneity in FL
- Decentralized FL
- Device heterogeneity in FL
- Fairness in FL
- Hardware for on-device FL
- Variants of FL like split learning
- Local methods in FL
- Nonconvex FL
- Operational challenges in FL
- Optimization advances in FL
- Partial participation in FL
- Personalization in FL
- Privacy concerns in FL
- Privacy-preserving methods for FL
- Resource-efficient FL
- Systems and infrastructure for FL
- Theoretical contributions to FL
- Uncertainty in FL
- Vertical FL

The workshop will have invited talks on a diverse set of topics related to FL. In addition, we plan to have an industrial panel (over Zoom) and booth (on GatherTown), where researchers from industry will talk about challenges and solutions from an industrial perspective.

More information on previous workshops can be found at http://www.federated-learning.org/.


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 are recommended (but not mandatory) to be no more than 6 pages long, excluding references, and follow ICML-21 template (https://icml.cc/Conferences/2021/StyleAuthorInstructions). Submissions are double-blind (author identity shall not 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=flicml21

If you have any enquiries, please email us at: flicml21@easychair.org


Organizing Committee
- Nathalie Baracaldo (IBM Research Almaden, USA)
- Olivia Choudhury (Amazon, USA)
- Gauri Joshi (Carnegie Mellon University, USA)
- Peter Richtárik (King Abdullah University of Science and Technology, Saudi Arabia)
- Praneeth Vepakomma (Massachusetts Institute of Technology, USA)
- Shiqiang Wang (IBM T. J. Watson Research Center, USA)
- Han Yu (Nanyang Technological University, Singapore)

Program Committee
- Ali Anwar (IBM Research, USA)
- Anran Li (University of Science and Technology of China, China)
- Ashkan Yousefpour (Facebook AI, USA)
- Aurélien Bellet (INRIA, France)
- Bing Luo (Chinese University of Hong Kong, Shenzhen, China)
- Bingsheng He (National University of Singapore, Singapore)
- Carlee Joe-Wong (Carnegie Mellon University, USA)
- Changchang Liu (IBM Research, USA)
- Chaoyang He (University of Southern California, USA)
- Chuizheng Meng (University of Southern California, USA)
- Dianbo Liu (Massachusetts Institute of Technology, USA)
- Dimitris Papailiopoulos (University of Wisconsin-Madison, USA)
- Farzin Haddadpour (Yale University, USA)
- Feng Yan (University of Nevada, Reno, USA)
- Filip Hanzely (Toyota Technological Institute at Chicago, USA)
- Graham Cormode (The University of Warwick, UK)
- Grigory Malinovsky (King Abdullah University of Science & Technology, Saudi Arabia)
- Hongyi Wang (University of Wisconsin-Madison, USA)
- Hongyuan Zhan (Facebook, USA)
- Jakub Konečný (Google, USA)
- Jia (Kevin) Liu (Ohio State University, USA)
- Jiankai Sun (ByteDance Inc., China)
- Jianyu Wang (Carnegie Mellon University, USA)
- Jie Ding (University of Minnesota, Twin Cities, USA)
- Jihong Park (Deakin University, Australia)
- Jinhyun So (University of Southern California, USA)
- Joshua Gardner (University of Michigan, USA)
- Kallista Bonawitz (Google, USA)
- Kevin Hsieh (Microsoft Research, USA)
- Kshitiz Malik (Facebook, USA)
- Lei Jiao (University of Oregon, USA)
- Lingjuan Lyu (Ant Financial, China)
- Martin Jaggi (Ecole Polytechnique Fédérale de Lausanne, Switzerland)
- Mehrdad Mahdavi (The Pennsylvania State University, USA)
- Mher Safaryan (King Abdullah University of Science & Technology, Saudi Arabia)
- Michael Rabbat (McGill University, Canada)
- Mingyi Hong (University of Minnesota, USA)
- Mingyue Ji (University of Utah, USA)
- Mingzhe Chen (Princeton University, USA)
- Nguyen Tran (The University of Sydney, Australia)
- Peter Kairouz (Google AI, USA)
- Rui Lin Chalmers (University of Technology, Sweden)
- Samuel Horvath (King Abdullah University of Science & Technology, Saudi Arabia)
- Sebastian Urban Stich (Ecole Polytechnique Fédérale de Lausanne, Switzerland)
- Sewoong Oh (University of Illinois at Urbana-Champaign, USA)
- Shahin Shahrampour (Texas A&M University, USA)
- Shangwei Guo (Chongqing University, China)
- Songtao Lu (IBM Research, USA)
- Songze Li (The Hong Kong University of Science and Technology, Hong Kong)
- Stacy Patterson (Rensselaer Polytechnic Institute, USA)
- Tara Javidi (University of California San Diego. USA)
- Theodoros Salonidis (IBM, USA)
- Tian Li (Carnegie Mellon University, USA)
- Tianyi Chen (Rensselaer Polytechnic Institute, USA)
- Tiffany Tuor (Imperial College London, UK)
- Victor Valls (Yale University, USA)
- Xiaohu Wu (Nanyang Technological University, Singapore)
- Xiaoli Tang (South China University of Technology, China)
- Yang Liu (Tsinghua University, China)
- Yi Zhou (IBM Research, USA)
- Yi Zhou (The University of Utah, USA)
- Yuang Jiang (Yale University, USA)
- Zehui Xiong (Singapore University of Technology and Design, Singapore)
- Zichen Chen (Joint NTU-WeBank Research Centre on Fintech, Singapore)