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ARTIFICIAL INTELLIGENCE
MACHINE LEARNING
DATA PRIVACY
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About |
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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. |
Call for Papers |
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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:
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Summary |
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FL-ICML 2020 : International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with ICML 2020 will take place in Vienna, Austria. It’s a 2 days event starting on Jul 17, 2020 (Friday) and will be winded up on Jul 18, 2020 (Saturday). FL-ICML 2020 falls under the following areas: ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, DATA PRIVACY, etc. Submissions for this Workshop can be made by May 17, 2020. Authors can expect the result of submission by May 31, 2020. Please check the official event website for possible changes before you make any travelling arrangements. Generally, events are strict with their deadlines. It is advisable to check the official website for all the deadlines. Other Details of the FL-ICML 2020
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Credits and Sources |
| [1] FL-ICML 2020 : International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with ICML 2020 |
