Federated prediction for scalable and privacy-preserved knowledge-based planning in radiotherapy

TL;DR

This paper introduces FedKBP+, a federated learning platform designed for radiotherapy planning that preserves patient privacy while enabling collaborative model training across multiple institutions.

Contribution

FedKBP+ provides a unified communication framework supporting both centralized and decentralized federated learning for radiotherapy prediction tasks.

Findings

FedKBP+ achieves high effectiveness and robustness in predictive tasks.

The platform supports communication across distributed and local workstations.

Decentralized FL via Peer-to-Peer exchange is feasible and efficient.

Abstract

Background: Deep learning has potential to improve the efficiency and consistency of radiation therapy planning, but clinical adoption is hindered by the limited model generalizability due to data scarcity and heterogeneity among institutions. Although aggregating data from different institutions could alleviate this problem, data sharing is a practical challenge due to concerns about patient data privacy and other technical obstacles. Purpose: This work aims to address this dilemma by developing FedKBP+, a comprehensive federated learning (FL) platform for predictive tasks in real-world applications in radiotherapy treatment planning. Methods: We implemented a unified communication stack based on Google Remote Procedure Call (gRPC) to support communication between participants whether located on the same workstation or distributed across multiple workstations. In addition to supporting the centralized FL strategies commonly available in existing open-source frameworks, FedKBP+ also provides a fully decentralized FL model where participants directly exchange model weights to each other through Peer-to-Peer communication. We evaluated FedKBP+ on three predictive tasks using scale-attention network (SA-Net) as the predictive model. Conclusions: Our results demonstrate that FedKBP+ is highly effective, efficient and robust, showing great potential as a federated learning platform for radiation therapy.