Abstract

Federated Learning is a distributed machine learning approach that enables geographically distributed data silos to collaboratively learn a joint machine learning model without sharing data. Most of the existing work operates on unstructured data, such as images or text, or on structured data assumed to be consistent across the different silos. However, silos often have different schemata, data formats, data values, and access patterns. The field of data integration has developed many methods to address these challenges, including techniques for data exchange and query rewriting using declarative schema mappings, and entity linkage. We propose an architectural vision for an end-to-end Federated Learning and Integration system, incorporating the critical steps of data harmonization and data imputation, to spur further research on the intersection of data management information systems and machine learning.

Date

May 15, 2023

Authors

Dimitris Stripelis, Jose Luis Ambite

Conference

7th International Workshop on Health Intelligence (W3PHIAI-23)

Publisher

arXiv preprint arXiv:2305.08985