Description
On this poster, we are going to present the research of the Institute for Data Engineering at TU Hamburg on the processing of very large data streams as well Federated Learning (FL). Federated Learning (FL) offers an alternative approach to centralized Machine Learning, by distributing the model generation across different entities. Thus, learning can be conducted close to the data sources, and only the learned model is shared with other entities. This leads to benefits both with regard to data privacy and communication overhead. In addition, we will present our work on Data Stream Processing, i.e., how to handle very large amounts of streaming data coming, e.g., from Internet of Things devices, in a resource-efficient way.
Keywords
Big Data
Machine Learning
Data Stream Processing
Federated Learning
