PhD Position in Topology Design for Decentralized Federated Learning
il y a 4 semaines
Research Initiative
This PhD thesis is part of Inria's research initiative on Federated Learning, FedMalin. The PhD candidate will join the NEO project-team, positioned at the intersection of Operations Research and Network Science.
Research Objectives
The goal of this PhD is to propose algorithms to design the communication topology for decentralized federated learning, minimizing the total training duration. Several settings will be considered, including pre-processing and dynamic topology design.
Key Activities
The candidate will investigate how to practically quantify the similarity of local data distributions during training and study the extension of existing results to asymmetric communication links and other distributed optimization algorithms.
Requirements
The candidate should have a solid mathematical background, particularly in optimization, and be knowledgeable in machine learning. Previous experiences with PyTorch or TensorFlow are a plus.
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 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
- Contribution to mutual insurance (subject to conditions)
Remuneration
Duration: 36 months
Location: Sophia Antipolis, France
Gross Salary per month: 2051€ brut per month (year 1 & 2) and 2158€ brut per month (year 3)
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