Phd Position F/m Privacy-preserving Collaborative Learning of Large Language Models Across Heterogeneous Learning Paradigms

Le descriptif de l’offre ci-dessous est en Anglais_

**Type de contrat**: CDD

**Niveau de diplôme exigé**: Bac + 5 ou équivalent

**Fonction**: Doctorant

**A propos du centre ou de la direction fonctionnelle**:
The Inria University of Lille centre, created in 2008, employs 360 people including 305 scientists in 15 research teams. Recognised for its strong involvement in the socio-economic development of the Hauts-De-France region, the Inria University of Lille centre pursues a close relationship with large companies and SMEs. By promoting synergies between researchers and industrialists, Inria participates in the transfer of skills and expertise in digital technologies and provides access to the best European and international research for the benefit of innovation and companies, particularly in the region.For more than 10 years, the Inria University of Lille centre has been located at the heart of Lille's university and scientific ecosystem, as well as at the heart of Frenchtech, with a technology showroom based on Avenue de Bretagne in Lille, on the EuraTechnologies site of economic excellence dedicated to information and communication technologies (ICT)

**Contexte et atouts du poste**:
The widespread deployment of Large Language Models (LLMs) has given rise to diverse adaptation paradigms that accommodate varying computational and infrastructural constraints. In addition to traditional full fine-tuning, emerging methods such as prompt-tuning, parameter-efficient tuning like LoRA (Low-Rank Adaptation), and in-context learning for model adaptation with reduced computational resources or without access to model weights. These approaches open up new possibilities for collaborative learning in privacy-sensitive contexts, where multiple clients aim to improve LLM performance without exposing their raw data.

This PhD thesis will focus on designing privacy-preserving collaborative learning strategies for LLMs, starting in a homogeneous setting, where all participants rely on the same adaptation paradigm. This initial step will build a foundation for tackling the more ambitious and impactful goal of heterogeneous collaboration, where clients operate under different adaptation regimes due to diverse privacy, computational, or architectural constraints. A central challenge of the project is to reconcile contributions from such heterogeneous clients in a unified learning process, while ensuring rigorous privacy guarantees—most notably through differential privacy (DP), which provides strong theoretical protections against data leakage. The thesis will also address the trade-offs between model utility and privacy risk and propose novel mechanisms specifically tailored to this multi-paradigm collaborative learning scenario.

**Mission confiée**:
The person hired will carry out original research toward a PhD on “Privacy-Preserving Collaborative Learning of Large Language Models Across Heterogeneous Learning Paradigms.” The research will involve designing novel collaborative protocols, formalizing privacy guarantees, and evaluating the impact of different learning paradigms on performance and privacy.

**Principales activités**:
**Compétences**:

- Master’s degree in computer science, artificial intelligence, machine learning, natural language
processing, applied mathematics, or a related field.
- Solid foundations in machine learning and natural language processing (NLP), including familiarity
with training and fine-tuning large language models (LLMs), as well as an understanding of recent
trends such as prompt-tuning and in-context learning.
- Experience or strong interest in collaborative learning.
- Familiarity with privacy-enhancing technologies, in particular differential privacy, is a plus.
- Strong programming skills, preferably in Python, and experience with machine learning frameworks
such as PyTorch or TensorFlow.
- Excellent oral and written English proficiency.

**Avantages**:

- 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 (sick children, moving home, etc.)
- 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
- Social security coverage

**Rémunération**:
2 200 € monthly gross salary

**Informations générales**:

- **Thème/Domaine**: Optimisation, apprentissage et méthodes statistiques
Calcul Scientifique (BAP E)
- **Ville**: Villeneuve d'Ascq
- **Centre Inria**: Centre Inria de l'Université de Lille
- **Date de prise de fonction souhaitée**: 2025-10-01
- **Durée de contrat**: 3 ans
- **Date limite pour postuler**: 2025-09-01

**Consignes pour postuler**:
Please send your CV and cover letter

**Sécurité défense**:
Ce poste est susceptible d’être affecté dans