FedHealth 2: Weighted Federated Transfer Learning via Batch Normalization for Personalized Healthcare

• URL: http://arxiv.org/abs/2106.01009v1
• Date: Wed, 2 Jun 2021 08:10:50 GMT
• Title: FedHealth 2: Weighted Federated Transfer Learning via Batch Normalization for Personalized Healthcare
• Authors: Yiqiang Chen, Wang Lu, Jindong Wang, Xin Qin
• Abstract summary: FedHealth 2 is an extension of FedHealth to tackle domain shifts and get personalized models for local clients. It can achieve better accuracy (10%+ improvement for activity recognition) and personalized healthcare without compromising privacy and security.
• Score: 10.350441801743855
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The success of machine learning applications often needs a large quantity of data. Recently, federated learning (FL) is attracting increasing attention due to the demand for data privacy and security, especially in the medical field. However, the performance of existing FL approaches often deteriorates when there exist domain shifts among clients, and few previous works focus on personalization in healthcare. In this article, we propose FedHealth 2, an extension of FedHealth \cite{chen2020fedhealth} to tackle domain shifts and get personalized models for local clients. FedHealth 2 obtains the client similarities via a pretrained model, and then it averages all weighted models with preserving local batch normalization. Wearable activity recognition and COVID-19 auxiliary diagnosis experiments have evaluated that FedHealth 2 can achieve better accuracy (10%+ improvement for activity recognition) and personalized healthcare without compromising privacy and security.

Related papers

• Can large language models be privacy preserving and fair medical coders? [13.49769820767045]
  Differential privacy (DP) is a common method for preserving privacy in such settings. We examine two key trade-offs in applying DP to the NLP task of medical coding (ICD classification)
  arXiv  Detail & Related papers  (2024-12-07T04:27:05Z)
• FedMetaMed: Federated Meta-Learning for Personalized Medication in Distributed Healthcare Systems [7.32609591220333]
  We introduce Federated Meta-Learning for Personalized Medication (FedMetaMed) FedMetaMed combines federated learning and meta-learning to create models that adapt to diverse patient data across healthcare systems. We show that FedMetaMed outperforms state-of-the-art FL methods, showing superior generalization even on out-of-the-art cohorts.
  arXiv  Detail & Related papers  (2024-12-05T03:36:55Z)
• Towards Privacy-Preserving Medical Imaging: Federated Learning with Differential Privacy and Secure Aggregation Using a Modified ResNet Architecture [0.0]
  This research introduces a federated learning framework that combines local differential privacy and secure aggregation. We also propose DPResNet, a modified ResNet architecture optimized for differential privacy.
  arXiv  Detail & Related papers  (2024-12-01T05:52:29Z)
• Which Client is Reliable?: A Reliable and Personalized Prompt-based Federated Learning for Medical Image Question Answering [51.26412822853409]
  We present a novel personalized federated learning (pFL) method for medical visual question answering (VQA) models. Our method introduces learnable prompts into a Transformer architecture to efficiently train it on diverse medical datasets without massive computational costs.
  arXiv  Detail & Related papers  (2024-10-23T00:31:17Z)
• FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge Injection [83.54960238236548]
  FEDMEKI not only preserves data privacy but also enhances the capability of medical foundation models. FEDMEKI allows medical foundation models to learn from a broader spectrum of medical knowledge without direct data exposure.
  arXiv  Detail & Related papers  (2024-08-17T15:18:56Z)
• Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshness [66.3982155172418]
  We first design a user-centric privacy-preserving framework based on decentralized Federated Learning (FL) for healthcare metaverses. We then utilize Age of Information (AoI) as an effective data-freshness metric and propose an AoI-based contract theory model under Prospect Theory (PT) to motivate sensing data sharing.
  arXiv  Detail & Related papers  (2023-07-29T12:54:03Z)
• Client-Level Differential Privacy via Adaptive Intermediary in Federated Medical Imaging [33.494287036763716]
  Trade-off of differential privacy (DP) between privacy protection and performance is still underexplored for real-world medical scenario. We propose to optimize the trade-off under the context of client-level DP, which focuses on privacy during communications. We propose an adaptive intermediary strategy to improve performance without harming privacy.
  arXiv  Detail & Related papers  (2023-07-24T06:12:37Z)
• Audit to Forget: A Unified Method to Revoke Patients' Private Data in Intelligent Healthcare [14.22413100609926]
  We developed AFS, which is able to evaluate and revoke patients' private data from pre-trained deep learning models. We demonstrated the generality of AFS by applying it to four tasks on different datasets.
  arXiv  Detail & Related papers  (2023-02-20T07:29:22Z)
• Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging [47.99192239793597]
  We evaluated the effect of privacy-preserving training of AI models regarding accuracy and fairness compared to non-private training. Our study shows that -- under the challenging realistic circumstances of a real-life clinical dataset -- the privacy-preserving training of diagnostic deep learning models is possible with excellent diagnostic accuracy and fairness.
  arXiv  Detail & Related papers  (2023-02-03T09:49:13Z)
• 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)
• Federated Learning with Adaptive Batchnorm for Personalized Healthcare [47.52430258876696]
  We propose AdaFed to tackle domain shifts and obtain personalized models for local clients. AdaFed learns the similarity between clients via the statistics of the batch normalization layers. Experiments on five healthcare benchmarks demonstrate that AdaFed achieves better accuracy compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-12-01T11:36:56Z)
• Personalized Federated Learning with Clustering: Non-IID Heart Rate Variability Data Application [0.1465840097113565]
  We propose Personalized Federated Cluster Models, a hierarchical clustering-based FL process, to predict Major Depressive Disorder severity from Heart Rate Variability. By allowing clients to receive more personalized model, we address problems caused by non-IID data, showing an accuracy increase in severity prediction.
  arXiv  Detail & Related papers  (2021-08-04T08:24:23Z)
• Anonymizing Data for Privacy-Preserving Federated Learning [3.3673553810697827]
  We propose the first syntactic approach for offering privacy in the context of federated learning. Our approach aims to maximize utility or model performance, while supporting a defensible level of privacy. We perform a comprehensive empirical evaluation on two important problems in the healthcare domain, using real-world electronic health data of 1 million patients.
  arXiv  Detail & Related papers  (2020-02-21T02:30:16Z)

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.