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

• URL: http://arxiv.org/abs/2505.14507v1
• Date: Tue, 20 May 2025 15:35:49 GMT
• Title: Federated prediction for scalable and privacy-preserved knowledge-based planning in radiotherapy
• Authors: Jingyun Chen, David Horowitz, Yading Yuan,
• Abstract summary: FedKBP+ is a comprehensive federated learning (FL) platform for predictive tasks in real-world applications in radiotherapy treatment planning.<n>We implemented a unified communication stack based on Google Remote Procedure Call (gRPC) to support communication between participants.<n>We evaluated FedKBP+ on three predictive tasks using scale-attention network (SA-Net) as the predictive model.
• Score: 0.5575343193009424
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• EPIC: Enhancing Privacy through Iterative Collaboration [4.199844472131922]
  Traditional machine learning techniques require centralized data collection and processing. Privacy, ownership, and stringent regulation issues exist when pooling medical data into centralized storage. The Federated learning (FL) approach overcomes such issues by setting up a central aggregator server and a shared global model.
  arXiv  Detail & Related papers  (2024-11-07T20:10:34Z)
• A Federated Learning Platform as a Service for Advancing Stroke Management in European Clinical Centers [37.285731240749904]
  This paper introduces a novel Federated Learning (FL) platform designed to support the configuration, monitoring, and management of FL processes. Considering the production sensitivity inherent in clinical environments, we emphasize the security of the proposed FL architecture. The platform has been successfully tested in various operational environments using a publicly available dataset.
  arXiv  Detail & Related papers  (2024-10-02T09:24:05Z)
• Communication-Efficient Hybrid Federated Learning for E-health with Horizontal and Vertical Data Partitioning [67.49221252724229]
  E-health allows smart devices and medical institutions to collaboratively collect patients' data, which is trained by Artificial Intelligence (AI) technologies to help doctors make diagnosis. Applying federated learning in e-health faces many challenges. Medical data is both horizontally and vertically partitioned. A naive combination of HFL and VFL has limitations including low training efficiency, unsound convergence analysis, and lack of parameter tuning strategies.
  arXiv  Detail & Related papers  (2024-04-15T19:45:07Z)
• Decentralized Learning over Wireless Networks: The Effect of Broadcast with Random Access [56.91063444859008]
  We investigate the impact of broadcast transmission and probabilistic random access policy on the convergence performance of D-SGD. Our results demonstrate that optimizing the access probability to maximize the expected number of successful links is a highly effective strategy for accelerating the system convergence.
  arXiv  Detail & Related papers  (2023-05-12T10:32:26Z)
• Personalizing Federated Learning with Over-the-Air Computations [84.8089761800994]
  Federated edge learning is a promising technology to deploy intelligence at the edge of wireless networks in a privacy-preserving manner. Under such a setting, multiple clients collaboratively train a global generic model under the coordination of an edge server. This paper presents a distributed training paradigm that employs analog over-the-air computation to address the communication bottleneck.
  arXiv  Detail & Related papers  (2023-02-24T08:41:19Z)
• Learning Personalized Brain Functional Connectivity of MDD Patients from Multiple Sites via Federated Bayesian Networks [9.873532358701803]
  We propose a federated joint estimator, NOTEARS-PFL, for simultaneous learning of multiple Bayesian networks. We evaluate the performance of the proposed method on both synthetic and real-world multi-site rs-fMRI datasets.
  arXiv  Detail & Related papers  (2023-01-06T08:58:06Z)
• Decentralized Distributed Learning with Privacy-Preserving Data Synthesis [9.276097219140073]
  In the medical field, multi-center collaborations are often sought to yield more generalizable findings by leveraging the heterogeneity of patient and clinical data. Recent privacy regulations hinder the possibility to share data, and consequently, to come up with machine learning-based solutions that support diagnosis and prognosis. We present a decentralized distributed method that integrates features from local nodes, providing models able to generalize across multiple datasets while maintaining privacy.
  arXiv  Detail & Related papers  (2022-06-20T23:49:38Z)
• Federated Offline Reinforcement Learning [55.326673977320574]
  We propose a multi-site Markov decision process model that allows for both homogeneous and heterogeneous effects across sites. We design the first federated policy optimization algorithm for offline RL with sample complexity. We give a theoretical guarantee for the proposed algorithm, where the suboptimality for the learned policies is comparable to the rate as if data is not distributed.
  arXiv  Detail & Related papers  (2022-06-11T18:03:26Z)
• DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training [84.81043932706375]
  We propose a novel personalized federated learning framework in a decentralized (peer-to-peer) communication protocol named Dis-PFL. Dis-PFL employs personalized sparse masks to customize sparse local models on the edge. We demonstrate that our method can easily adapt to heterogeneous local clients with varying computation complexities.
  arXiv  Detail & Related papers  (2022-06-01T02:20:57Z)
• Communication-Efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced Data [42.26599494940002]
  Federated learning (FL) is a distributed learning methodology that allows multiple nodes to cooperatively train a deep learning model. This paper studies the potential of hierarchical FL in IoT heterogeneous systems. It proposes an optimized solution for user assignment and resource allocation on multiple edge nodes.
  arXiv  Detail & Related papers  (2021-07-14T08:32:39Z)
• Decentralized MCTS via Learned Teammate Models [89.24858306636816]
  We present a trainable online decentralized planning algorithm based on decentralized Monte Carlo Tree Search. We show that deep learning and convolutional neural networks can be employed to produce accurate policy approximators.
  arXiv  Detail & Related papers  (2020-03-19T13:10:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.