International Workshop on Federated Learning: Recent Advances and New Challenges in Conjunction with NeurIPS 2022
FL-NeurIPS 2022
- URL: https://federated-learning.org/fl-neurips-2022/
- Event Date: 2022-12-02 ~ 2022-12-02
- Submission Date: 2022-09-15
[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.
[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.
[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)
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.
[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.
[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)
Journal Promotion
|
| Acta Koreana (Acta Koreana) |
|
| Acta Via Serica (AVS) |
|
| Computers and Concrete (CAC) |
|
| Journal of Information Processing Systems (JIPS) |
|
| Journal of Internet Computing and Services (KSII - JICS) |
|
| Journal of Smart Tourism (Smart Tourism) |
|
| Membrane and Water Treatment (MWT) |
|
| Wind and Structures, An International Journal (Wind and Structures) |
MANUSCRIPTLINK © 2015 -
