Postdoctoral Research Visit F/M Privacy-preserving and Robust Federated Learning: Investigating the Intersection of Differential Privacy and Byzantine Resilience in Federated Learning

il y a 4 semaines


Technopole de Sophia Antipolis, France INRIA Temps plein

Context and Key Features of the Position

The position involves investigating the intersection of differential privacy and Byzantine resilience in federated learning, with a focus on developing novel algorithms that can seamlessly integrate these two security measures with low computational complexity.

Key Responsibilities

The successful candidate will be responsible for:

  • Designing and implementing novel federated learning algorithms that combine differential privacy and Byzantine resilience
  • Investigating the impact of compression on the trade-off among privacy, robustness, and model utility
  • Developing theoretical and experimental results to illustrate the effectiveness of the proposed methods

Requirements

The candidate should have a solid mathematical background, good programming skills, and previous experience with PyTorch or TensorFlow. They should also be knowledgeable on machine learning, especially federated learning, and have good analytical skills. Fluency in English is required.

Benefits

  • Subsidized meals
  • Partial reimbursement of public transport costs
  • Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave
  • Possibility of teleworking (after 6 months of employment) 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
  • Social security coverage

Salary

Gross Salary: 2788 € per month



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