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Post-doc Development of algorithms for decentralized, resilient federated learning H/F


Détail de l'offre

Informations générales

Entité de rattachement

Le Commissariat à l'énergie atomique et aux énergies alternatives (CEA) est un organisme public de recherche.

Acteur majeur de la recherche, du développement et de l'innovation, le CEA intervient dans le cadre de ses quatre missions :
. la défense et la sécurité
. l'énergie nucléaire (fission et fusion)
. la recherche technologique pour l'industrie
. la recherche fondamentale (sciences de la matière et sciences de la vie).

Avec ses 16000 salariés -techniciens, ingénieurs, chercheurs, et personnel en soutien à la recherche- le CEA participe à de nombreux projets de collaboration aux côtés de ses partenaires académiques et industriels.  

Référence

2021-17271  

Description du poste

Domaine

Mathématiques, information  scientifique, logiciel

Contrat

Post-doctorat

Intitulé de l'offre

Post-doc Development of algorithms for decentralized, resilient federated learning H/F

Sujet de stage

Development of algorithms for decentralized, resilient federated learning

Durée du contrat (en mois)

12

Description de l'offre

The postdoctoral fellow will join the Carnot FANTASTYC project which puts together researchers on distributed ledger technology, privacy and machine learning with the aim of developing software assets for decentralized, privacy-preserving and resilient federated learning.

Federated learning (FL) is a machine learning setting in which many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized (communicating only the model parameters). Hence, in traditional federated learning, a central server orchestrates the
training process and receives the contributions of all clients and hence represents a single point of failure and/or a communication bottleneck. Against this background, the first objective of this fellowship is to envisage a fully decentralized efficient version of the federated learning, replacing
communication with the server by peer-to-peer communication between individual clients on some communication graph. Note that in this peer-to-peer setting there is no longer a global state of the model, but the process can be designed such that all local models converge to the desired global solution, i.e., the individual models gradually reach consensus. On doing that, the successful
applicant is expected to tackle some of the open challenges that involve passing to decentralized learning, including: (1) the design, specification and implementation of efficient decentralised learning protocols; (2) the evaluation of communication and computational costs of protocols on different network topologies possibly leading to the design of new resource-aware distributed
learning protocols; and (3) tackling the compromise between generic and personalized models depending on the evaluated non-IID of data distributions available to individual clients (e.g. different models for clusters of participants). For the design and implementation of the distributed framework the post-doc is expected to collaborate with other CEA labs involved in the project that will be
providing a privacy-preserving distributed ledger technology infrastructure. The other focus of this position will be the study of the robustness of distributed federated learning against the presence of malicious participants (i.e. Byzantine attacks).
The application domain envisaged in the project is personalized privacy-preserving health monitoring.

Qualifications :
1. PhD in machine learning from an accredited university
2. Excellent communication skills, both verbal and written in English
3. Communication skills in French
4. Experience in federated learning is a plus
5. Experience in distributed systems and/or robust attacks is a plus

To apply send an updated CV and a motivation letter to:
Cédric Gouy-Pailler (cedric.gouy-pailler@cea.fr)
Aurélien Mayoue (aurelien.mayoue@cea.fr)
Meritxell Vinyals (meritxell.vinyals@cea.fr)

Localisation du poste

Site

Saclay

Localisation du poste

France

Ville

saclay