19/04/2021

Post-doc : Development of algorithms for decentralized, resilient federated learning.


  • ORGANISATION/COMPANY
    French Alternative Energies and Atomic Energy Commission (CEA)
  • RESEARCH FIELD
    Computer scienceComputer systems
  • RESEARCHER PROFILE
    First Stage Researcher (R1)
  • APPLICATION DEADLINE
    15/06/2021 00:00 - Europe/Athens
  • LOCATION
    France › GIF-SUR-YVETTE
  • TYPE OF CONTRACT
    Temporary
  • JOB STATUS
    Full-time
  • HOURS PER WEEK
    40
  • OFFER STARTING DATE
    01/09/2021

OFFER DESCRIPTION

Offer description

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) [1]. 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) [2,3].

The application domain envisaged in the project is personalized privacy-preserving health monitoring.

[1] Google AI blog: https://ai.googleblog.com/2017/04/federated-learning-collaborative.html

[2] Blanco-Justicia, A., Domingo-Ferrer, J., Martínez, S., Sánchez, D., Flanagan, A., & Tan, K. E. (2020). Achieving Security and Privacy in Federated Learning Systems: Survey, Research Challenges and Future Directions. arXiv preprint arXiv:2012.06810.

[3] Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui, Julien Stainer: Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent. NIPS 2017: 119-129

Application and contacts

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)

More Information

Benefits

Salary & benefits

The salary will depend on the applicant’s profile and experience. The position comes with various social benefits (e.g. health insurance). This position is open for one year, renewable once.

Selection process

The position is open immediately (April 2021). Review of applications will begin as soon as applications are received and continue until the position is filled.

Offer Requirements

  • REQUIRED EDUCATION LEVEL
    Computer science: PhD or equivalent
  • REQUIRED LANGUAGES
    ENGLISH: Excellent
    FRENCH: Good

Skills/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

Map Information

Job Work Location Personal Assistance locations
Work location(s)
1 position(s) available at
CEA-LIST
France
GIF-SUR-YVETTE
91191
CEA SACLAY

EURAXESS offer ID: 630750

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