SHK: Distributed Machine Learning

Distributed Machine Learning

Centralized computation for machine learning (ML) tasks can be compute intensive and also requires input data that might not be available for sharing due to various reasons such as privacy or available communication bandwidth. This gap is filled by the concept of distributed machine learning that refers to multi-node machine learning systems that bring the model to the data (not the other way around) and hence train, collect and aggregate model instances in repetitive synchronization steps.

For more information on Flower : A Friendly Federated Learning Framework


  • Completed or curretly doing a bachelor's degree in computer science or electrical engineering and information technology.
  • Hands-on expirence with Linux
  • Programming Knowledge in Python
  • Good English writing skills

Main Tasks

  • Implementation of Federated Machine Learning (FML) in an automotive environment
  • Using Flower (FLM framework) to execute and evaluate FML tasks

  • Design and code APIs for node interactions in Flower.


Start time

As soon as possible

Contract duration

Up to 1 year

Work time

10 hours per week