Postdoctoral Research Fellow in Federated Learning and Secure Machine Learning
il y a 2 semaines
In this project, we aim to develop novel federated learning algorithms that effectively address the challenges of data privacy and security in machine learning. We will explore the potentialities of compression in FL training, which can highly reduce the model dimension and provide a solution for a computation-efficient, private, and secure FL system.
Research ObjectivesThe main objectives of this project are:
- To develop novel federated learning algorithms that integrate differential privacy and Byzantine resilience with low computational complexity.
- To study the impact of compression on the trade-off among privacy, robustness, and model utility in FL systems.
- To design and implement a compression strategy that provides the best trade-off among privacy, robustness, computational complexity, and model performance.
We will employ a combination of theoretical and experimental approaches to achieve the research objectives. We will first develop novel federated learning algorithms that integrate differential privacy and Byzantine resilience with low computational complexity. We will then study the impact of compression on the trade-off among privacy, robustness, and model utility in FL systems. Finally, we will design and implement a compression strategy that provides the best trade-off among privacy, robustness, computational complexity, and model performance.
Expected OutcomesThe expected outcomes of this project are:
- Novel federated learning algorithms that integrate differential privacy and Byzantine resilience with low computational complexity.
- A comprehensive understanding of the impact of compression on the trade-off among privacy, robustness, and model utility in FL systems.
- A compression strategy that provides the best trade-off among privacy, robustness, computational complexity, and model performance.
The candidate should have a solid mathematical background, good programming skills, and previous experience with PyTorch or TensorFlow. He/She should also be knowledgeable on machine learning, especially federated learning, and have good analytical skills. We expect the candidate to be fluent in English.
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Postdoctoral Researcher in Federated Learning
il y a 3 semaines
Technopole de Sophia Antipolis, France INRIA Temps pleinAbout INRIA INRIA is a leading French research institute dedicated to advancing computer science and digital technologies. We foster innovation through cutting-edge research, collaborative projects, and the training of future generations of scientists. Research Focus: Privacy-Preserving Machine Learning Our team specializes in developing novel techniques...
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Research Scientist in Federated Learning and Robust Machine Learning
il y a 2 semaines
Technopole de Sophia Antipolis, France INRIA Temps pleinAbout the ProjectIn this project, we aim to develop novel federated learning algorithms that effectively address the challenges of data privacy and security in machine learning. We will explore the potentialities of compression in FL training, which can highly reduce the model dimension and provide a solution for a computation-efficient, private, and secure...
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Technopole de Sophia Antipolis, France INRIA Temps pleinJob DescriptionWe are seeking a highly skilled postdoctoral researcher to join our team at INRIA, working on distributed machine learning and edge AI. The successful candidate will be part of the dAIEDGE project, a European Union-funded network of excellence for distributed, trustworthy, efficient, and scalable AI at the edge.Research TopicsThe postdoctoral...
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il y a 3 semaines
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il y a 1 semaine
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il y a 2 semaines
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il y a 2 semaines
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il y a 15 heures
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il y a 3 semaines
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