Heterogeneous Federated Learning System for Sparse Healthcare Time-Series Prediction

• URL: http://arxiv.org/abs/2501.12125v1
• Date: Tue, 21 Jan 2025 13:38:06 GMT
• Title: Heterogeneous Federated Learning System for Sparse Healthcare Time-Series Prediction
• Authors: Jia-Hao Syu, Jerry Chun-Wei Lin,
• Abstract summary: We design dense and sparse feature tensors to deal with sparsity of data sources. Heterogeneous federated learning is developed to share asynchronous parts of networks and select appropriate models for knowledge transfer.
• Score: 26.197000069634207
• License:
• Abstract: In this paper, we propose a heterogeneous federated learning (HFL) system for sparse time series prediction in healthcare, which is a decentralized federated learning algorithm with heterogeneous transfers. We design dense and sparse feature tensors to deal with the sparsity of data sources. Heterogeneous federated learning is developed to share asynchronous parts of networks and select appropriate models for knowledge transfer. Experimental results show that the proposed HFL achieves the lowest prediction error among all benchmark systems on eight out of ten prediction tasks, with MSE reduction of 94.8%, 48.3%, and 52.1% compared to the benchmark systems. These results demonstrate the effectiveness of HFL in transferring knowledge from heterogeneous domains, especially in the smaller target domain. Ablation studies then demonstrate the effectiveness of the designed mechanisms for heterogeneous domain selection and switching in predicting healthcare time series with privacy, model security, and heterogeneous knowledge transfer.

Related papers

• Heterogeneous Federated Learning Systems for Time-Series Power Consumption Prediction with Multi-Head Embedding Mechanism [45.85115919116268]
  Time-series prediction is increasingly popular in a variety of applications, such as smart factories and smart transportation. Existing models lack discussion of collaborative learning and privacy issues among multiple clients. We propose Multi-Head Heterogeneous Federated Learning (MHHFL) systems that consist of multiple head networks.
  arXiv  Detail & Related papers  (2025-01-21T13:49:38Z)
• Comparative Evaluation of Clustered Federated Learning Methods [0.5242869847419834]
  Clustered Federated Learning (CFL) aims to partition clients into groups where the distribution are homogeneous. In this paper, we explore the performance of two state-of-theart CFL algorithms with respect to a proposed taxonomy of data heterogeneities in federated learning (FL) Our objective is to provide a clearer understanding of the relationship between CFL performances and data heterogeneous scenarios.
  arXiv  Detail & Related papers  (2024-10-18T07:01:56Z)
• Tackling Computational Heterogeneity in FL: A Few Theoretical Insights [68.8204255655161]
  We introduce and analyse a novel aggregation framework that allows for formalizing and tackling computational heterogeneous data. Proposed aggregation algorithms are extensively analyzed from a theoretical, and an experimental prospective.
  arXiv  Detail & Related papers  (2023-07-12T16:28:21Z)
• A Federated Learning-based Industrial Health Prognostics for Heterogeneous Edge Devices using Matched Feature Extraction [16.337207503536384]
  We propose a pioneering FL-based health prognostic model with a feature similarity-matched parameter aggregation algorithm. We show that the proposed method yields accuracy improvements as high as 44.5% and 39.3% for state-of-health estimation and remaining useful life estimation.
  arXiv  Detail & Related papers  (2023-05-13T07:20:31Z)
• Magnitude Matters: Fixing SIGNSGD Through Magnitude-Aware Sparsification in the Presence of Data Heterogeneity [60.791736094073]
  Communication overhead has become one of the major bottlenecks in the distributed training of deep neural networks. We propose a magnitude-driven sparsification scheme, which addresses the non-convergence issue of SIGNSGD. The proposed scheme is validated through experiments on Fashion-MNIST, CIFAR-10, and CIFAR-100 datasets.
  arXiv  Detail & Related papers  (2023-02-19T17:42:35Z)
• Depersonalized Federated Learning: Tackling Statistical Heterogeneity by Alternating Stochastic Gradient Descent [6.394263208820851]
  Federated learning (FL) enables devices to train a common machine learning (ML) model for intelligent inference without data sharing. Raw data held by various cooperativelyicipators are always non-identically distributedly. We propose a new FL that can significantly statistical optimize by the de-speed of this process.
  arXiv  Detail & Related papers  (2022-10-07T10:30:39Z)
• Heterogeneous Federated Learning via Grouped Sequential-to-Parallel Training [60.892342868936865]
  Federated learning (FL) is a rapidly growing privacy-preserving collaborative machine learning paradigm. We propose a data heterogeneous-robust FL approach, FedGSP, to address this challenge. We show that FedGSP improves the accuracy by 3.7% on average compared with seven state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-01-31T03:15:28Z)
• An Experimental Study of Data Heterogeneity in Federated Learning Methods for Medical Imaging [8.984706828657814]
  Federated learning enables multiple institutions to collaboratively train machine learning models on their local data in a privacy-preserving way. We investigate the deleterious impact of a taxonomy of data heterogeneity regimes on federated learning methods, including quantity skew, label distribution skew, and imaging acquisition skew. We present several mitigation strategies to overcome performance drops from data heterogeneity, including weighted average for data quantity skew, weighted loss and batch normalization averaging for label distribution skew.
  arXiv  Detail & Related papers  (2021-07-18T05:47:48Z)
• Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data [62.29031007761901]
  This paper proposes a novel contrastive regularized clinical classification model. We introduce two unique positive sampling strategies specifically tailored for EHR data. Our framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data.
  arXiv  Detail & Related papers  (2021-04-07T06:02:04Z)
• Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees [49.91477656517431]
  Quantization-based solvers have been widely adopted in Federated Learning (FL) No existing methods enjoy all the aforementioned properties. We propose an intuitively-simple yet theoretically-simple method based on SIGNSGD to bridge the gap.
  arXiv  Detail & Related papers  (2020-02-25T15:12:15Z)

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.