PhD Researcher in Federated Learning for 6G Wireless Communication Systems
Il y a 2 mois
Context and Opportunities
This PhD position is part of the Nokia-Inria Federated Learning Challenge, hosted within the MARACAS team in the CITI Laboratory. The position will be supervised by Dr Malcolm Egan and Prof. Jean-Marie Gorce (MARACAS) and Dr Alberto Conte and Marie-Line Alberia Morel (Networks Systems and Security Research, Nokia Bell Labs).
MARACAS is a research group focused on communication systems, developing and applying methods in information theory, statistical signal processing, and machine learning. The PhD position will complement existing projects on stochastic optimization and federated learning within MARACAS.
Research Focus
The Inria-Nokia Federated Learning Challenge project aims to develop federated and decentralized learning architectures and algorithms for future generation wireless communication systems. Federated learning systems support model estimation without providing client data directly to the server. Instead, clients estimate local models and exchange local model parameters or associated gradients with the server.
In this PhD position, we will investigate the impact of realistic wireless communication links on state-of-the-art federated learning methods and develop new resource allocation, compression, and coding schemes to optimize tradeoffs between reliability and compression of federated learning updates.
Main Responsibilities
- Analyze the convergence of state-of-the-art federated learning methods in the presence of realistic limitations arising from emerging 6G wireless communication systems.
- Develop new coding, resource allocation, and compression schemes to mitigate the impact of errors arising from wireless communications.
Requirements
- Background in optimization theory, probability theory/statistics, and wireless communications.
- Ideally exposure to stochastic optimization algorithms and the corresponding convergence theory.
- Proficiency in Python and ideally experience with machine learning packages.
Benefits
- Subsidized meals
- Partial reimbursement of public transport costs
- Leave: 7 weeks of annual leave + 10 extra days off due to RTT + possibility of exceptional leave
- Possibility of teleworking (90 days/year) and flexible organization of working hours
- Professional equipment available (videoconferencing, loan of computer equipment, etc.)
- Social, cultural, and sports events and activities
- Access to vocational training
- Complementary health insurance under conditions
Salary
1st and 2nd year: 2,100 euros gross salary/month
3rd year: 2,190 euros gross salary/month
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Villeurbanne, Auvergne-Rhône-Alpes, France INRIA Temps pleinAbout the ProjectThis PhD position is part of the Nokia-Inria Federated Learning Challenge and will be hosted within the MARACAS team in the CITI Laboratory, a joint laboratory between Inria and INSA-Lyon. The position will be supervised by Dr Malcolm Egan and Prof. Jean-Marie Gorce (MARACAS) and Dr Alberto Conte and Marie-Line Alberia Morel (Networks...
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Villeurbanne, Auvergne-Rhône-Alpes, France INRIA Temps pleinAbout the ProjectThis PhD position is part of the Nokia-Inria Federated Learning Challenge and will be hosted within the MARACAS team in the CITI Laboratory, a joint laboratory between Inria and INSA-Lyon. The position will be supervised by Dr Malcolm Egan and Prof. Jean-Marie Gorce (MARACAS) and Dr Alberto Conte and Marie-Line Alberia Morel (Networks...
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Villeurbanne, Auvergne-Rhône-Alpes, France INRIA Temps pleinAbout the Position This PhD position is part of the Nokia-Inria Federated Learning Challenge, hosted within the MARACAS team at CITI Laboratory (a joint venture between Inria and INSA-Lyon). You will be supervised by Dr. Malcolm Egan and Prof. Jean-Marie Gorce (MARACAS), alongside Dr. Alberto Conte and Marie-Line Alberia Morel (Networks Systems and Security...
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Villeurbanne, Auvergne-Rhône-Alpes, France INRIA Temps pleinJob DescriptionThis PhD position is part of the Nokia-Inria Federated Learning Challenge project, which aims to develop federated and decentralized learning architectures and algorithms for future generation wireless communication systems.Context and ObjectivesThe PhD student will work on the development of federated learning algorithms for wireless...
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