Federated Learning over Harmonized Data Silos
Dimitris Stripelis Jose Luis Ambite
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 sites. However, sites 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 for entity linkage. Therefore, 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.