Election of Collaborators via Reinforcement Learning for Federated Brain Tumor Segmentation

• URL: http://arxiv.org/abs/2412.20253v1
• Date: Sat, 28 Dec 2024 19:54:06 GMT
• Title: Election of Collaborators via Reinforcement Learning for Federated Brain Tumor Segmentation
• Authors: Muhammad Irfan Khan, Elina Kontio, Suleiman A. Khan, Mojtaba Jafaritadi,
• Abstract summary: Federated learning (FL) enables collaborative model training across decentralized datasets. We present RL-HSimAgg, a novel reinforcement learning (RL) and similarity-weighted aggregation (simAgg) algorithm. We demonstrate the effectiveness of Epsilon-greedy (EG) and upper confidence bound (UCB) algorithms for federated brain lesion segmentation.
• Score: 0.1969973131266619
• License:
• Abstract: Federated learning (FL) enables collaborative model training across decentralized datasets while preserving data privacy. However, optimally selecting participating collaborators in dynamic FL environments remains challenging. We present RL-HSimAgg, a novel reinforcement learning (RL) and similarity-weighted aggregation (simAgg) algorithm using harmonic mean to manage outlier data points. This paper proposes applying multi-armed bandit algorithms to improve collaborator selection and model generalization. By balancing exploration-exploitation trade-offs, these RL methods can promote resource-efficient training with diverse datasets. We demonstrate the effectiveness of Epsilon-greedy (EG) and upper confidence bound (UCB) algorithms for federated brain lesion segmentation. In simulation experiments on internal and external validation sets, RL-HSimAgg with UCB collaborator outperformed the EG method across all metrics, achieving higher Dice scores for Enhancing Tumor (0.7334 vs 0.6797), Tumor Core (0.7432 vs 0.6821), and Whole Tumor (0.8252 vs 0.7931) segmentation. Therefore, for the Federated Tumor Segmentation Challenge (FeTS 2024), we consider UCB as our primary client selection approach in federated Glioblastoma lesion segmentation of multi-modal MRIs. In conclusion, our research demonstrates that RL-based collaborator management, e.g. using UCB, can potentially improve model robustness and flexibility in distributed learning environments, particularly in domains like brain tumor segmentation.

Related papers

• Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings [4.022491041135248]
  This study develops a deep learning ensemble that integrates UNet3D, V-Net, and MSA-VNet models for the semantic segmentation of gliomas. Our ensemble approach significantly outperforms individual models, achieving DICE scores of 0.8358 for Tumor Core, 0.8521 for Whole Tumor, and 0.8167 for Enhancing Tumor.
  arXiv  Detail & Related papers  (2025-02-04T09:53:09Z)
• Recommender Engine Driven Client Selection in Federated Brain Tumor Segmentation [0.1969973131266619]
  This study presents a robust and efficient client selection protocol for the Federated Tumor Challenge (FeTS 2024) New or inactive collaborators pose selection challenges due to limited data. We propose harmonic similarity weight aggregation (HSimAgg) for adaptive aggregation of model parameters.
  arXiv  Detail & Related papers  (2024-12-28T19:49:02Z)
• Multicenter Privacy-Preserving Model Training for Deep Learning Brain Metastases Autosegmentation [2.479757014250359]
  This work aims to explore the impact of multicenter data heterogeneity on deep learning brain metastases (BM) autosegmentation performance. incremental transfer learning technique, namely learning without forgetting (LWF), to improve model generalizability without sharing raw data. When the UKER pretrained model is applied to USZ, LWF achieves a higher average F1 score (0.839) than naive TL (0.570) and single-center training (0.688) on combined UKER and USZ test data.
  arXiv  Detail & Related papers  (2024-05-17T16:01:11Z)
• Source-Free Collaborative Domain Adaptation via Multi-Perspective Feature Enrichment for Functional MRI Analysis [55.03872260158717]
  Resting-state MRI functional (rs-fMRI) is increasingly employed in multi-site research to aid neurological disorder analysis. Many methods have been proposed to reduce fMRI heterogeneity between source and target domains. But acquiring source data is challenging due to concerns and/or data storage burdens in multi-site studies. We design a source-free collaborative domain adaptation framework for fMRI analysis, where only a pretrained source model and unlabeled target data are accessible.
  arXiv  Detail & Related papers  (2023-08-24T01:30:18Z)
• A Reinforcement Learning-assisted Genetic Programming Algorithm for Team Formation Problem Considering Person-Job Matching [70.28786574064694]
  A reinforcement learning-assisted genetic programming algorithm (RL-GP) is proposed to enhance the quality of solutions. The hyper-heuristic rules obtained through efficient learning can be utilized as decision-making aids when forming project teams.
  arXiv  Detail & Related papers  (2023-04-08T14:32:12Z)
• Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning [92.18524491615548]
  Contrastive self-supervised learning has been successfully integrated into the practice of (deep) reinforcement learning (RL) We study how RL can be empowered by contrastive learning in a class of Markov decision processes (MDPs) and Markov games (MGs) with low-rank transitions. Under the online setting, we propose novel upper confidence bound (UCB)-type algorithms that incorporate such a contrastive loss with online RL algorithms for MDPs or MGs.
  arXiv  Detail & Related papers  (2022-07-29T17:29:08Z)
• Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation [48.821062916381685]
  Federated learning (FL) is a distributed machine learning technique that enables collaborative model training while avoiding explicit data sharing. In this work, we propose an efficient reinforcement learning(RL)-based federated hyperparameter optimization algorithm, termed Auto-FedRL. The effectiveness of the proposed method is validated on a heterogeneous data split of the CIFAR-10 dataset and two real-world medical image segmentation datasets.
  arXiv  Detail & Related papers  (2022-03-12T04:11:42Z)
• Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor Segmentation [2.294014185517203]
  We focus on the federated learning paradigm, a distributed learning approach for decentralized data. Studies show that federated learning can provide competitive performance with conventional central training. We explore different strategies for faster convergence and better performance which can also work on strong Non-IID cases.
  arXiv  Detail & Related papers  (2022-02-16T07:49:04Z)
• Cross-Modality Deep Feature Learning for Brain Tumor Segmentation [158.8192041981564]
  This paper proposes a novel cross-modality deep feature learning framework to segment brain tumors from the multi-modality MRI data. The core idea is to mine rich patterns across the multi-modality data to make up for the insufficient data scale. Comprehensive experiments are conducted on the BraTS benchmarks, which show that the proposed cross-modality deep feature learning framework can effectively improve the brain tumor segmentation performance.
  arXiv  Detail & Related papers  (2022-01-07T07:46:01Z)
• Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution [56.17099252139182]
  We automate and standardize the task of brain tumor segmentation with U-net like neural networks. Two independent ensembles of models were trained, and each produced a brain tumor segmentation map. Our solution achieved a Dice of 0.79, 0.89 and 0.84, as well as Hausdorff 95% of 20.4, 6.7 and 19.5mm on the final test dataset.
  arXiv  Detail & Related papers  (2020-10-30T14:36:10Z)

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.