dsLassoCov: a federated machine learning approach incorporating covariate control

• URL: http://arxiv.org/abs/2412.07991v1
• Date: Wed, 11 Dec 2024 00:03:52 GMT
• Title: dsLassoCov: a federated machine learning approach incorporating covariate control
• Authors: Han Cao, Augusto Anguita, Charline Warembourg, Xavier Escriba-Montagut, Martine Vrijheid, Juan R. Gonzalez, Tim Cadman, Verena Schneider-Lindner, Daniel Durstewitz, Xavier Basagana, Emanuel Schwarz,
• Abstract summary: Federated learning allows the direct, privacy preserving training of machine learning models using geographically distributed datasets.<n>We introduce dsLassoCov, a machine learning approach designed to control for covariate effects.<n>We demonstrate that dsLassoCov can efficiently and effectively manage confounding effects during model training.
• Score: 3.2203047133326645
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning has been widely adopted in biomedical research, fueled by the increasing availability of data. However, integrating datasets across institutions is challenging due to legal restrictions and data governance complexities. Federated learning allows the direct, privacy preserving training of machine learning models using geographically distributed datasets, but faces the challenge of how to appropriately control for covariate effects. The naive implementation of conventional covariate control methods in federated learning scenarios is often impractical due to the substantial communication costs, particularly with high-dimensional data. To address this issue, we introduce dsLassoCov, a machine learning approach designed to control for covariate effects and allow an efficient training in federated learning. In biomedical analysis, this allow the biomarker selection against the confounding effects. Using simulated data, we demonstrate that dsLassoCov can efficiently and effectively manage confounding effects during model training. In our real-world data analysis, we replicated a large-scale Exposome analysis using data from six geographically distinct databases, achieving results consistent with previous studies. By resolving the challenge of covariate control, our proposed approach can accelerate the application of federated learning in large-scale biomedical studies.

Related papers

• DWFL: Enhancing Federated Learning through Dynamic Weighted Averaging [2.499907423888049]
  We propose a deep feed-forward neural network-based enhanced federated learning method for protein sequence classification. We introduce dynamic weighted federated learning (DWFL) which is a federated learning-based approach. We conduct experiments using real-world protein sequence datasets to assess the effectiveness of DWFL.
  arXiv  Detail & Related papers  (2024-11-07T20:24:23Z)
• Multi-OCT-SelfNet: Integrating Self-Supervised Learning with Multi-Source Data Fusion for Enhanced Multi-Class Retinal Disease Classification [2.5091334993691206]
  Development of a robust deep-learning model for retinal disease diagnosis requires a substantial dataset for training. The capacity to generalize effectively on smaller datasets remains a persistent challenge. We've combined a wide range of data sources to improve performance and generalization to new data.
  arXiv  Detail & Related papers  (2024-09-17T17:22:35Z)
• Targeted Cause Discovery with Data-Driven Learning [66.86881771339145]
  We propose a novel machine learning approach for inferring causal variables of a target variable from observations. We employ a neural network trained to identify causality through supervised learning on simulated data. Empirical results demonstrate the effectiveness of our method in identifying causal relationships within large-scale gene regulatory networks.
  arXiv  Detail & Related papers  (2024-08-29T02:21:11Z)
• Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets [53.367212596352324]
  We propose an unsupervised approach leveraging EEG signal physics. We map EEG channels to fixed positions using field, source-free domain adaptation. Our method demonstrates robust performance in brain-computer interface (BCI) tasks and potential biomarker applications.
  arXiv  Detail & Related papers  (2024-03-07T16:17:33Z)
• Decentralized Learning with Multi-Headed Distillation [12.90857834791378]
  Decentralized learning with private data is a central problem in machine learning. We propose a novel distillation-based decentralized learning technique that allows multiple agents with private non-iid data to learn from each other.
  arXiv  Detail & Related papers  (2022-11-28T21:01:43Z)
• When Accuracy Meets Privacy: Two-Stage Federated Transfer Learning Framework in Classification of Medical Images on Limited Data: A COVID-19 Case Study [77.34726150561087]
  COVID-19 pandemic has spread rapidly and caused a shortage of global medical resources. CNN has been widely utilized and verified in analyzing medical images.
  arXiv  Detail & Related papers  (2022-03-24T02:09:41Z)
• Enhancing ensemble learning and transfer learning in multimodal data analysis by adaptive dimensionality reduction [10.646114896709717]
  In multimodal data analysis, not all observations would show the same level of reliability or information quality. We propose an adaptive approach for dimensionality reduction to overcome this issue. We test our approach on multimodal datasets acquired in diverse research fields.
  arXiv  Detail & Related papers  (2021-05-08T11:53:12Z)
• FLOP: Federated Learning on Medical Datasets using Partial Networks [84.54663831520853]
  COVID-19 Disease due to the novel coronavirus has caused a shortage of medical resources. Different data-driven deep learning models have been developed to mitigate the diagnosis of COVID-19. The data itself is still scarce due to patient privacy concerns. We propose a simple yet effective algorithm, named textbfFederated textbfL textbfon Medical datasets using textbfPartial Networks (FLOP)
  arXiv  Detail & Related papers  (2021-02-10T01:56:58Z)
• Uncovering the structure of clinical EEG signals with self-supervised learning [64.4754948595556]
  Supervised learning paradigms are often limited by the amount of labeled data that is available. This phenomenon is particularly problematic in clinically-relevant data, such as electroencephalography (EEG) By extracting information from unlabeled data, it might be possible to reach competitive performance with deep neural networks.
  arXiv  Detail & Related papers  (2020-07-31T14:34:47Z)
• A Trainable Optimal Transport Embedding for Feature Aggregation and its Relationship to Attention [96.77554122595578]
  We introduce a parametrized representation of fixed size, which embeds and then aggregates elements from a given input set according to the optimal transport plan between the set and a trainable reference. Our approach scales to large datasets and allows end-to-end training of the reference, while also providing a simple unsupervised learning mechanism with small computational cost.
  arXiv  Detail & Related papers  (2020-06-22T08:35:58Z)
• Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results [13.615292855384729]
  To train a high-quality deep learning model, the aggregation of a significant amount of patient information is required. Due to the need to protect the privacy of patient data, it is hard to assemble a central database from multiple institutions. Federated learning allows for population-level models to be trained without centralizing entities' data.
  arXiv  Detail & Related papers  (2020-01-16T04:49:33Z)

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.