General information
Organisation
The French Alternative Energies and Atomic Energy Commission (CEA) is a key player in research, development and innovation in four main areas :
• defence and security,
• nuclear energy (fission and fusion),
• technological research for industry,
• fundamental research in the physical sciences and life sciences.
Drawing on its widely acknowledged expertise, and thanks to its 16000 technicians, engineers, researchers and staff, the CEA actively participates in collaborative projects with a large number of academic and industrial partners.
The CEA is established in ten centers spread throughout France
Reference
SL-DRT-25-0869
Direction
DRT
Thesis topic details
Category
Technological challenges
Thesis topics
Enabling efficient federated learning and fine-tuning for heterogeneous and resource-constrained devices
Contract
Thèse
Job description
The goal of this PhD thesis is to develop methods that enhance resource efficiency in federated learning (FL), with particular attention to the constraints and heterogeneity of client resources. The work will first focus on the classical client-server FL architecture, before extending the investigation to decentralised FL settings. The proposed methods will be studied in the context of both federated model training and distributed fine-tuning of large models, such as large language models (LLMs).
University / doctoral school
Sciences et Technologies de l’Information et de la Communication (STIC)
Paris-Saclay
Thesis topic location
Site
Saclay
Requester
Person to be contacted by the applicant
KEM Oudom 
oudom.kem@cea.fr
CEA
DRT/DIN//LIIDE
CEA Saclay
Bâtiment 565, PC 192
91 191 Gif-sur-Yvette
Tutor / Responsible thesis director
SOULOUMIAC Antoine antoine.souloumiac@cea.fr
CEA
DRT/DIN//LIIDE
Bât. 565, pièce 2043 - PC192
CEA/SACLAY
01 69 08 49 76
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