FedWSIDD: Federated Whole Slide Image Classification via Dataset Distillation
Journal:
arXiv
Published Date:
Jun 18, 2025
Abstract
Federated learning (FL) has emerged as a promising approach for collaborative
medical image analysis, enabling multiple institutions to build robust
predictive models while preserving sensitive patient data. In the context of
Whole Slide Image (WSI) classification, FL faces significant challenges,
including heterogeneous computational resources across participating medical
institutes and privacy concerns. To address these challenges, we propose
FedWSIDD, a novel FL paradigm that leverages dataset distillation (DD) to learn
and transmit synthetic slides. On the server side, FedWSIDD aggregates
synthetic slides from participating centres and distributes them across all
centres. On the client side, we introduce a novel DD algorithm tailored to
histopathology datasets which incorporates stain normalisation into the
distillation process to generate a compact set of highly informative synthetic
slides. These synthetic slides, rather than model parameters, are transmitted
to the server. After communication, the received synthetic slides are combined
with original slides for local tasks. Extensive experiments on multiple WSI
classification tasks, including CAMELYON16 and CAMELYON17, demonstrate that
FedWSIDD offers flexibility for heterogeneous local models, enhances local WSI
classification performance, and preserves patient privacy. This makes it a
highly effective solution for complex WSI classification tasks. The code is
available at FedWSIDD.
