posted by organizer: yuhanpanda || 1179 views || tracked by 4 users: [display]

FL-NeurIPS 2022 : International Workshop on Federated Learning: Recent Advances and New Challenges in Conjunction with NeurIPS 2022


When Dec 2, 2022 - Dec 2, 2022
Where New Orleans, LA, USA
Submission Deadline Sep 22, 2022
Notification Due Oct 20, 2022
Categories    artificial intelligence   machine learning   federated learning   trustworthy computing

Call For Papers

[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:
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:

[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]
- Ali Anwar (University of Minnesota)
- Ang Li (Duke University)
- Anran Li (Nanyang Technological University)
- Ashkan Yousefpour (Meta)
- Aurélien Bellet (INRIA)
- 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)
- Chuizheng Meng (University of Southern California)
- Dianbo Liu (University of Montreal)
- Divyansh Jhunjhunwala (Carnegie Mellon University)
- Farzin Haddadpour (Yale University)
- Grigory Malinovsky (KAUST)
- Hongyi Wang (Carnegie Mellon University)
- Hongyuan Zhan (Meta)
- Jayanth Reddy Regatti (Ohio State University)
- Jia Liu (Ohio State University)
- Jiankai Sun (ByteDance Inc.)
- Jianyu Wang (Facebook)
- Jiayi Wang (University of Utah)
- Jihong Park (Deakin University)
- Jinhyun So (University of Southern California)
- Kallista Bonawitz (Google)
- Kevin Hsieh (Microsoft)
- Konstantin Mishchenko (Ecole Normale Supérieure de Paris)
- Kshitiz Malik (University of Illinois, Urbana-Champaign)
- Mehrdad Mahdavi (Pennsylvania State University)
- Mi Zhang (Ohio State University)
- Michael Rabbat (McGill University)
- Mingyi Hong (Iowa State University)
- Mingzhe Chen (University of Miami)
- Ningning Ding (Chinese University of Hong Kong)
- Paulo Ferreira (Dell)
- Pengchao Han (Chinese University of Hong Kong, Shenzhen)
- Pranay Sharma (Carnegie Mellon University)
- Rui Lin (Chalmers University of Technology)
- Samuel Horváth (Mohamed bin Zayed University of Artificial Intelligence)
- Sebastian U Stich (CISPA Helmholtz Center for Information Security)
- Shangwei Guo (Chongqing University)
- Songtao Lu (IBM Research)
- Songze Li (The Hong Kong University of Science and Technology)
- Swanand Kadhe (IBM Research)
- Tara Javidi (University of California, San Diego)
- Theodoros Salonidis (IBM Research)
- Tianyi Chen (Rensselaer Polytechnic Institute)
- Victor Valls (Trinity College, Dublin)
- Yae Jee Cho (Carnegie Mellon University)
- Yang Liu (Tsinghua University)
- Yi Zhou (IBM Research)
- Zehui Xiong (Singapore University of Technology and Design)
- Zheng Xu (Google)

Related Resources

FAIML 2023   2023 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML 2023)
NeurIPS 2022   Thirty-sixth Conference on Neural Information Processing Systems
MLDM 2023   18th International Conference on Machine Learning and Data Mining
RAMFP 2022   TAA (OA) - SI: Recent Advances on Metric Fixed Point Theory 2022
IJCNN 2023   International Joint Conference on Neural Networks
MaDaIN 2022   The 3rd International conference on Recent Advances in Machine Learning, Data Science, Intelligent Systems & Networking
CFMAI 2022   2022 4th International Conference on Frontiers of Mathematics and Artificial Intelligence (CFMAI 2022)
FL-IJCAI 2022   International Workshop on Trustworthy Federated Learning in Conjunction with IJCAI 2022 (FL-IJCAI'22)
NeurIPS 2022   Robustness in Sequence Modeling
EI-CFAIS 2022   2022 International Conference on Frontiers of Artificial Intelligence and Statistics (CFAIS 2022)