International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with ICML 2020
FL-ICML 2020
- URL: http://federated-learning.org/fl-icml-2020/
- Event Date: 2020-07-17 ~ 2020-07-18
- Submission Date: 2020-05-17
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 federated learning, 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. Topics of interest include, but are not limited to, the following:
- Adversarial attacks on FL
- Blockchain for FL
- Fairness in FL
- Hardware for on-device FL
- Novel applications of FL
- Operational challenges in FL
- Personalization in FL
- Privacy concerns in FL
- Privacy-preserving methods for FL
- Resource-efficient FL
- System and infrastructure for FL
- Theoretical contributions to FL
- Uncertainty in FL
- Submission Instructions
Submissions must be at most 6 pages long, excluding references, and follow ICML-20 template. We would not accept submission of work currently under review.
Easychair submission link: https://easychair.org/conferences/?conf=flicml20
If you have any enquiries, please email us at: flworkshop.icml.2020@gmail.com
Organizing Committee
- Olivia Choudhury (IBM Research Cambridge, USA)
- Gauri Joshi (Carnegie Mellon University, USA)
- Han Yu (Nanyang Technological University, Singapore)
- Nathalie Baracaldo (IBM Research Almaden, USA)
- Ramesh Raskar (MIT Media Lab, USA)
- Shiqiang Wang (IBM T. J. Watson Research Center, USA)
Program Committee
- Mikhail Yurochkin (IBM Research, USA)
- Jihong Park (Deakin University, Australia)
- Yang Liu (Webank, China)
- Andrew Trask (DeepMind, USA)
- Mingyi Hong (University of Minnesota, USA)
- Jakub Konečný (Google, USA)
- Mehdi Bennis (University of Oulu, Finland)
- Lingfei Wu (IBM Research AI, USA)
- Peter Richtarik (King Abdullah University of Science and Technology, Saudi Arabia)
- Ji Liu (Stony Brook University, USA)
- Boi Faltings (Ecole Polytechnique Fédérale de Lausanne, Switzerland)
- Supriyo Chakraborty (IBM Research, USA)
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 federated learning, 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. Topics of interest include, but are not limited to, the following:
- Adversarial attacks on FL
- Blockchain for FL
- Fairness in FL
- Hardware for on-device FL
- Novel applications of FL
- Operational challenges in FL
- Personalization in FL
- Privacy concerns in FL
- Privacy-preserving methods for FL
- Resource-efficient FL
- System and infrastructure for FL
- Theoretical contributions to FL
- Uncertainty in FL
- Submission Instructions
Submissions must be at most 6 pages long, excluding references, and follow ICML-20 template. We would not accept submission of work currently under review.
Easychair submission link: https://easychair.org/conferences/?conf=flicml20
If you have any enquiries, please email us at: flworkshop.icml.2020@gmail.com
Organizing Committee
- Olivia Choudhury (IBM Research Cambridge, USA)
- Gauri Joshi (Carnegie Mellon University, USA)
- Han Yu (Nanyang Technological University, Singapore)
- Nathalie Baracaldo (IBM Research Almaden, USA)
- Ramesh Raskar (MIT Media Lab, USA)
- Shiqiang Wang (IBM T. J. Watson Research Center, USA)
Program Committee
- Mikhail Yurochkin (IBM Research, USA)
- Jihong Park (Deakin University, Australia)
- Yang Liu (Webank, China)
- Andrew Trask (DeepMind, USA)
- Mingyi Hong (University of Minnesota, USA)
- Jakub Konečný (Google, USA)
- Mehdi Bennis (University of Oulu, Finland)
- Lingfei Wu (IBM Research AI, USA)
- Peter Richtarik (King Abdullah University of Science and Technology, Saudi Arabia)
- Ji Liu (Stony Brook University, USA)
- Boi Faltings (Ecole Polytechnique Fédérale de Lausanne, Switzerland)
- Supriyo Chakraborty (IBM Research, USA)
Journal Promotion
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| Acta Koreana (Acta Koreana) |
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| Acta Via Serica (AVS) |
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| Computers and Concrete (CAC) |
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| Journal of Information Processing Systems (JIPS) |
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| Journal of Internet Computing and Services (KSII - JICS) |
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| Journal of Smart Tourism (Smart Tourism) |
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| Membrane and Water Treatment (MWT) |
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| Wind and Structures, An International Journal (Wind and Structures) |
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