mlsanityData to 14 Oct 2026

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

arXiv 2505.14507 ↗First posted 20 May 2025cs.DC cs.LG physics.med-phcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 49 references that are in the map. First posted on arXiv 20 May 2025.

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17 of 49 references.

References · 17

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5 more
  1. 2022Byzantine-Robust Decentralized Learning via ClippedGossipcited by 31
  2. 2020OpenKBP: The open-access knowledge-based planning grand challengecited by 30
  3. 2021A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guaranteecited by 16
  4. 2020Automatic Brain Tumor Segmentation with Scale Attention Networkcited by 8
  5. 2025Decentralized Personalization for Federated Medical Image Segmentation via Gossip Contrastive Mutual Learningcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Making Cross-Continental Federated Learning Repeatable with FLIP: a Multi-Application Studycited by 0