PhD Position in Topology Design for Decentralized Federated Learning

il y a 5 jours


Technopole de Sophia Antipolis, France INRIA Temps plein
Context and Objectives

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 Focus

The research activity will focus on proposing algorithms to design the communication topology for decentralized federated learning, with the goal of minimizing the total training duration. This will involve considering various settings, including pre-processing and dynamic topology design, as well as investigating how to practically quantify the similarity of local data distributions during training.

Expected Outcomes

The PhD candidate is expected to contribute to the development of novel algorithms and methods for decentralized federated learning, with a focus on topology design and data distribution similarity. The research outcomes will be published in top-tier conferences and journals.

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. Fluency in English is required.

Benefits

The PhD position offers a gross salary of 2051€ per month (year 1 & 2) and 2158€ per month (year 3). The position also includes subsidized meals, partial reimbursement of public transport costs, and access to vocational training.



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