[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 machine learning 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 and booth, where researchers from industry will talk about challenges and solutions from an industrial perspective.

[Submission Instructions]
Submissions should be no more than 6 pages long, excluding references, and follow NeurIPS'22 template. Submissions are double-blind (author identity shall not be revealed to the reviewers), so the submitted PDF file should not include any identifiable information of authors. An optional appendix of any length is allowed and should be put at the end of the paper (after references).

Submissions are collected on OpenReview at the following link: https://openreview.net/group?id=NeurIPS.cc/2022/Workshop/Federated_Learning.
Accepted papers and their review comments will be posted on OpenReview in public. Due to the short timeline, we will not have a rebuttal period, but the authors are encouraged to interact and discuss with reviewers on OpenReview after the acceptance notifications are sent out. Rejected papers and their reviews will remain private and not posted in public.

For questions, please contact: fl-neurips-2022@googlegroups.com

[Proceedings and Dual Submission Policy]
Our workshop does not have formal proceedings, i.e., it is non-archival. Accepted papers will be available in public on OpenReview together with the reviewers' comments. Revisions to accepted papers will be allowed until shortly before the workshop date.

We welcome submissions of unpublished papers, including those that are submitted to other venues if that other venue allows so. However, papers that have been accepted to an archival venue as of Sept. 21, 2022 should not be resubmitted to this workshop, because the goal of the workshop is to share recent results and discuss open problems. Specifically, papers that have been accepted to NeurIPS'22 main conference should not be resubmitted to this workshop.

[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]
- Aditya Balu (Iowa State University)
- Ali Anwar (University of Minnesota)
- Alp Yurtsever (Umea University)
- Ambrish Rawat (IBM Research)
- Anastasios Kyrillidis (Rice University)
- Andre Manoel (Microsoft)
- Andrew Silva (Georgia Institute of Technology)
- Ang Li (Duke University)
- Anran Li (Nanyang Technological University)
- Ashkan Yousefpour (Meta)
- Aurélien Bellet (INRIA)
- Berivan Isik (Amazon)
- Bing Luo (Duke University)
- Bingsheng He (National University of Singapore)
- Carlee Joe-Wong (Carnegie Mellon University)
- Chao Ren (Nanyang Technological University)
- Chaoyang He (University of Southern California)
- Chuan Xu (INRIA)
- Chuizheng Meng (University of Southern California)
- Chulin Xie (University of Illinois, Urbana Champaign)
- Dianbo Liu (University of Montreal)
- Dimitrios Dimitriadis (Microsoft Research)
- Divyansh Jhunjhunwala (Carnegie Mellon University)
- Egor Shulgin (KAUST)
- Enmao Diao (Duke University)
- Farzin Haddadpour (Yale University)
- Feng Yan (University of Houston)
- Giovanni Neglia (INRIA)
- Giulio Zizzo (IBM Research)
- Grigory Malinovsky (KAUST)
- Haibo Yang (Ohio State University)
- Hongyi Wang (Carnegie Mellon University)
- Hongyuan Zhan (Meta)
- Javier Fernandez-Marques (Samsung AI)
- Jayanth Reddy Regatti (Ohio State University)
- Jesse C Cresswell (Layer 6 AI)
- Jia Liu (Ohio State University)
- Jiankai Sun (ByteDance Inc.)
- Jianyu Wang (Facebook)
- Jiayi Wang (University of Utah)
- Jihong Park (Deakin University)
- Jinghui Chen (Pennsylvania State University)
- Jinhyun So (University of Southern California)
- John Nguyen (Facebook)
- Junyi Li (University of Pittsburgh)
- Kshitiz Malik (University of Illinois, Urbana-Champaign)
- Kai Yi (KAUST)
- Kallista Bonawitz (Google)
- Kamalika Chaudhuri (Facebook)
- Kevin Hsieh (Microsoft)
- Konstantin Mishchenko (Ecole Normale Supérieure de Paris)
- Lie He (Swiss Federal Institute of Technology Lausanne)
- Lingjuan Lyu (Sony AI)
- Mathieu Even (INRIA)
- Matthias Reisser (Qualcomm)
- Mehrdad Mahdavi (Pennsylvania State University)
- Mi Zhang (Ohio State University)
- Michael Kamp (Institute for AI in Medicine IKIM)
- Michael Rabbat (McGill University)
- Michal Yemini (Princeton University)
- Mingyi Hong (Iowa State University)
- Mingzhe Chen (University of Miami)
- Minhao Cheng (Hong Kong University of Science and Technology)
- M. Taha Toghani (Rice University)
- Nikola Konstantinov (ETH Zurich)
- Ningning Ding (Northwestern University)
- Pranay Sharma (Carnegie Mellon University)
- Paulo Abelha Ferreira (Dell Technologies)
- Pengchao Han (Chinese University of Hong Kong, Shenzhen)
- Peter Kairouz (Google)
- Pierre Stock (Facebook)
- Prashant Khanduri (Wayne State University)
- Radu Marculescu (University of Texas, Austin)
- Rui Lin (Chalmers University of Technology)
- Ruihan Wu (Cornell University)
- Saeed Vahidian (University of California, San Diego)
- Sai Praneeth Karimireddy (University of California, Berkeley)
- Samuel Horváth (Mohamed bin Zayed University of Artificial Intelligence)
- Satoshi Hara (Osaka University)
- Sayak Mukherjee (Pacific Northwest National Laboratory)
- Se-Young Yun (KAIST)
- Sebastian U Stich (CISPA Helmholtz Center for Information Security)
- Shangwei Guo (Chongqing University)
- Songtao Lu (IBM Research)
- Songze Li (Hong Kong University of Science and Technology)
- Stefanos Laskaridis (Samsung AI Center Cambridge)
- Swanand Kadhe (IBM Research)
- Tahseen Rabbani (University of Maryland, College Park)
- Tara Javidi (University of California, San Diego)
- Theodoros Salonidis (IBM Research)
- Tianyi Chen (Rensselaer Polytechnic Institute)
- Victor Valls (Trinity College, Dublin)
- Virendra Marathe (Oracle)
- Wenshuo Guo (University of California, Berkeley)
- Xiang Yu (NEC)
- Xiaoyong Yuan (Michigan Technological University)
- Yae Jee Cho (Carnegie Mellon University)
- Yang Liu (Tsinghua University)
- Yi Zhou (IBM Research)
- Zachary Charles (Google)
- Zehui Xiong (Singapore University of Technology and Design)
- Zhanhong Jiang (Johnson Controls Inc.)
- Zhaozhuo Xu (Rice University)
- Zheng Xu (Google)