Recent Methodological Advances in Federated Learning for Healthcare
Fan Zhang Daniel Kreuter Yichen Chen Sören Dittmer Samuel Tull Tolou Shadbahr BloodCounts! Collaboration Jacobus Preller James H. F. Rudd John A. D. Aston Carola-Bibiane Schönlieb Nicholas Gleadall Michael Roberts
For healthcare datasets, it is often not possible to combine data samples
from multiple sites due to ethical, privacy or logistical concerns. Federated
learning allows for the utilisation of powerful machine learning algorithms
without requiring the pooling of data. Healthcare data has many simultaneous
challenges which require new methodologies to address, such as highly-siloed
data, class imbalance, missing data, distribution shifts and non-standardised
variables. Federated learning adds significant methodological complexity to
conventional centralised machine learning, requiring distributed optimisation,
communication between nodes, aggregation of models and redistribution of
models. In this systematic review, we consider all papers on Scopus that were
published between January 2015 and February 2023 and which describe new
federated learning methodologies for addressing challenges with healthcare
data. We performed a detailed review of the 89 papers which fulfilled these
criteria. Significant systemic issues were identified throughout the literature
which compromise the methodologies in many of the papers reviewed. We give
detailed recommendations to help improve the quality of the methodology
development for federated learning in healthcare.