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Master thesis: Semi-supervised federated learning of deep neural networks – RISE, Sweden – Nov 2021

RISE Research Institutes of Sweden


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Oct 31, 2021 23:59 (GMT +2)

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RISE Research Institutes of Sweden seeks Master thesis: Semi-supervised federated learning of deep neural networks at the department of Intelligenta System – Sweden, Nov 2021

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General Info

Position: Internships
No. of Positions: 1
Research Field:
Deadline to Apply: October 31, 2021(GMT +2)
Joining Date: -
Contract Period: 3-6 months
Salary: According to Standard Norms

Department of Intelligenta System
RISE Research Institutes of Sweden

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Qualification Details

We expect you to have required skills:

  • Experience of implementing machine learning models.
  • Courses in mathematical statistics, probability theory or similar.
  • Programming skills. Preferably with some experience of relevant frameworks such as Pytorch or Tensorflow.

Responsibilities/Job Description

In this master thesis, you will work on semi-supervised federated learning, and investigate methods to learn useful deep learning models. You will work in close collaboration with our deep learning research group in Gothenburg. The work requires skilled students within machine learning and statistical inference. You will be expected to do a literature study in order to get familiar with what the field looks like today, and then start with simpler models and eventually extend or develop upon more advanced solutions.

How to Apply?

Online Application through "Apply Now" Button from this page or from advertisement webpage (URL below)

Reference Number: -2021/468
(If any, use it in the necessary place)

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Documents Required

Documents required to complete the online application process

About the Project

Federated learning (FL) has achieved a lot of attention in the past few years. FL is a distributed machine learning framework that promises efficiency and privacy benefits in settings where data is distributed among many clients. Although federated learning shows significant promise as a key solution when data cannot be shared, current methods show limited privacy properties and have shortcomings when applied to common real-world scenarios.

Given weights for a neural network, we want to minimize the sum of the expected loss over all clients. A central server is coordinating training between the clients. The most prevalent algorithm to solve this optimization problem is federated averaging. Each client has its own client model, which is trained on a local dataset. When all clients have completed the training, their weights are sent to the central server where they are aggregated into a global model using layer-wise averaging.

Most existing work on FL has been done on supervised learning, which requires that clients have labels associated with each training data point. Extending FL to unsupervised and semi-supervised settings where we have no, or very few labels, to train on present interesting and open challenges. In real world scenarios, there often is a high cost to label data, requiring some expert knowledge. Furthermore, client data is considered private and outsourcing this to some external party for labelling would not be possible. Therefore, client data in real world applications is often partly or completely unlabeled. However, if some labeled data is available at the central server, this could be utilized. This motivates research on federated learning in unsupervised, semi-supervised and self-supervised settings.

About the Employer: RISE Research Institutes of Sweden

Note or Other details

Candidates are encouraged to send in their application as soon as possible. Suitable applicants will be interviewed as applications are received.

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Contact details

For questions and more information, please contact recruiting manager Edvin Listo Zec, 0737200960.

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