Making Cross-Continental Federated Learning Repeatable with FLIP: a Multi-Application Study

2026-09-28

AI Summary

This study addresses the high governance reconfiguration overhead and poor experimental reproducibility in medical federated learning by proposing FLIP, an open-source platform built upon a microservice architecture. FLIP integrates DICOM retrieval, cohort querying, and independent node approval mechanisms, centering on operation-level reproducibility and site-level governance to establish a novel paradigm for intercontinental collaboration that standardizes and composes training and evaluation workflows. Validation on chest X-ray datasets from the United Kingdom and Thailand demonstrates that FLIP substantially enhances the auditability of federated learning and improves the efficiency of multi-center collaboration.

Abstract

Federated learning (FL) in healthcare remains challenging, as the overhead of rebuilding governance guarantees for every collaboration stops most projects at the proof-of-concept stage. Here we present FLIP (Federated Learning Interoperability Platform), an open-source, multi-application platform that makes FL training and evaluation repeatable. FLIP implements common FL workflows as a set of composable services: cohort queries against per-site structured databases, on-demand DICOM retrieval from institutional PACS, per-site project approval, and reusable FL job types. To demonstrate FLIP, we ran two distinct use cases, federated fine-tuning and federated evaluation, on synthetic chest X-ray cohorts across two client nodes based in the United Kingdom (UK) and Thailand. In FLIP, each institution independently approves its participation in each project and operates its own node under local IT security processes. This study makes an operational rather than an algorithmic claim. It does not compare federated with centralised training; for that question, we refer the reader to existing systematic reviews and meta-analyses. The central result is evidence that such platforms enable international FL collaboration and improve repeatability, auditability, and site-specific governance. We also present a comprehensive comparison of existing platforms to help researchers and operators choose the right platform for their use case.

Problem

• Federated Learning
• Healthcare
• Repeatability
• Governance
• Cross-Continental Collaboration

Innovation

• Federated Learning
• Interoperability Platform
• Healthcare AI
• Reproducibility
• Composable Services