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Related Experiment Videos

Layer-based personalized multi-fusion federated learning.

Wangzhuo Yang1,2, Bo Chen3,4, Zheming Wang1,2

  • 1Department of Automation, Zhejiang University of Technology, Hangzhou, 310115, China.

Scientific Reports
|November 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a federated learning framework to build information system models without sensitive client data. It enhances collaborative personalization across diverse datasets, improving model accuracy.

Related Experiment Videos

Area of Science:

  • Machine Learning
  • Data Privacy
  • Information System Modeling

Background:

  • Information system modeling relies on historical data, but clients often withhold it due to privacy and security concerns.
  • Lack of data prevents the identification of models that provide optimal decisions for all clients.
  • Existing methods struggle to effectively utilize decentralized and heterogeneous data for model training.

Purpose of the Study:

  • To propose a novel federated learning framework addressing data privacy and security challenges in information system modeling.
  • To develop a multi-layer, multi-fusion strategy for client models within a federated learning environment.
  • To enhance collaborative personalization and decision-making accuracy across heterogeneous client datasets.

Main Methods:

  • A multi-layer, multi-fusion federated learning framework is proposed, deploying distinct fusion strategies for different neural network layers.
  • Neural network layers are functionally categorized into generalized feature extraction and personalized fully-connected layers.
  • An exponential similarity metric calculates fusion weights for the fully-connected layer, while the feature extraction layer uses a federated global optimal model approximation fusion strategy.

Main Results:

  • The proposed framework significantly outperforms existing comparable approaches in information system modeling.
  • Experimental results show an improvement in recognition accuracy on the CIFAR-100 dataset from 0.4792 to 0.4859.
  • The method effectively enhances collaborative personalization efficiency across heterogeneous data.

Conclusions:

  • The developed federated learning framework successfully overcomes data privacy and security barriers in information system modeling.
  • The multi-layer, multi-fusion strategy enables effective utilization of decentralized data for improved model performance.
  • This approach offers a robust solution for building accurate information system models in privacy-sensitive, data-scarce scenarios.