Leveraging Domain Adaptation in Unsupervised Federated Learning

il y a 9 heures

Rouen, France University of Rouen Normandy Temps plein

**Leveraging Domain Adaptation in Unsupervised Federated Learning**:

- Réf **ABG-134073**
- Stage master 2 / Ingénieur
- Durée 6 mois
- Salaire net mensuel 630
- 28/10/2025
- University of Rouen Normandy
- Lieu de travail- Rouen Normandie France
- Champs scientifiques- Science de la donnée (stockage, sécurité, mesure, analyse)
- Mathématiques
- Mots clés- Federated Learning, Source-Free Domain Adaptation, Class Imbalance, Unsupervised Learning- 31/01/2026

**Établissement recruteur**:
The internship will take place at the LITIS laboratory, which has 160 members—including PhD students—and seven teams covering a broad range of fundamental and applied research. The intern will join the “Apprentissage” team, consisting of 18 faculty members from Rouen University and INSA and approximately 20 PhD students. The team focuses on machine learning and pattern recognition for interpreting diverse data such as signals, images, and text.

**Description**:
Context

Federated learning (FL) enables models to learn from distributed datasets across diverse clients (e.g., edge devices, hospitals, or industrial sites) while maintaining privacy [1]. A major challenge in supervised FL is statistical variability, which causes client distribution shifts—differences in data distributions across clients [2]. Additionally, many clients contain unlabeled data that remain unused due to annotation costs, inconsistent labeling protocols, and model updates. As a result, FL with unlabeled data has gained attention, with approaches that take advantage of clustering techniques to correct client shifts [3,4].

Since domain shifts in Domain Adaptation (DA) are analogous to client shifts in federated learning, federated domain adaptation has naturally emerged to transfer methodological advances from DA to address distribution shifts [5]. DA seeks to learn a domain-invariant feature space between labeled source and target domains, whereas unsupervised domain adaptation handles the more challenging case where target data are unlabeled [6]. Recent studies have incorporated DA techniques into FL, primarily in centralized settings (e.g., at the server) to address evolving distribution shifts [7]. Among these methods, Source-Free Domain Adaptation (SFDA) presents a promising direction, as it enables a client to share a trained model with another party, allowing the recipient to perform unsupervised adaptation using their own data in conjunction with the shared model [8].

Objectives

**Profil**:
**Prise de fonction**:

- 01/02/2026

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