Workshop Federated Learning

Federated Learning Workshop: Decentralized Machine Learning That Ensures Data Privacy

The quality of algorithms is predominantly related to the amount of data available. In recent years, this availability has increased rapidly. In particular, the Internet of Things (IoT) generates large amounts of data from millions of devices. This makes it possible to train increasingly advanced machine learning models. In practice, however, making this data available to train a centralized model can be problematic due to regulatory restrictions or technical hurdles in transmitting large amounts of data over low bandwidths. One solution to these challenges is Federated Learning. 

Federated Learning – An Answer for Companies in Terms of Artificial Intelligence (AI)

Here, all training data is stored exclusively on local devices or clients and model training is decentralized. Clients receive a model and improve it by learning from their local data. A summary of a client's updated model is sent to a server via encrypted communication. There, it is aggregated with updates from other clients to form a common global model, that is, fused into a single global model. This model is then sent to the clients again, and the training process continues until the model has been sufficiently trained. 

Federated Learning in Project Use

We have already successfully used Federated Learning as part of the »Bauhaus Mobility Lab« (BML) project. The goal of the project is to build a digital lab platform that combines AI technologies with data. The project is being funded by the German Federal Ministry of Economics and Climate Protection (BMWK) in the double-digit million range until 2023 as part of the innovation competition »Artificial Intelligence as a Driver of Economically Relevant Ecosystems«.

Course Content

The full-day workshop will consist of several presentations on different aspects of Federated Learning followed by discussion sessions.
The following topics will be covered:

  • Introduction to Federated Learning – basic concepts and use cases.
  • Algorithms and variations of Federated Learning
  • System design and design pattern
  • Data protection and data security, incusive methods to ensure data protection
  • Existing frameworks and demo