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Related Experiment Videos

Intelligent Federated Learning Framework for Non-colocated and Heterogeneous Datasets.

Nilesh D Navghare1, L Mary Gladence2, Amol A Bhosle3

  • 1Sathyabama Institute of Science and Technology; nileshnavghare93@gmail.com.

Journal of Visualized Experiments : Jove
|April 27, 2026
PubMed
Summary

Intelligent Federated Learning Framework (IFLF) enhances privacy-preserving distributed training. It achieves stable convergence and high accuracy (92.8%) on diverse datasets by addressing heterogeneity challenges.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Data Science

Background:

  • Federated learning enables distributed model training while preserving data privacy.
  • Heterogeneity in non-colocated datasets presents challenges in convergence, fairness, and interpretability.

Purpose of the Study:

  • To propose an Intelligent Federated Learning Framework (IFLF) to address federated learning challenges.
  • To enhance convergence, fairness, and interpretability in distributed model training.

Main Methods:

  • A multi-layer architecture (Data, Client, Aggregation, Adaptation, Optimization, Interpretability) was developed.
  • Learning-rate modulation and Explainable AI (SHAP, LIME) techniques were integrated.
  • The framework was evaluated on diverse benchmark datasets: FEMNIST, FLamby, FedGraphNN, and CICIDS2017.

Main Results:

  • The IFLF demonstrated stable convergence under non-IID data distributions.
  • Aggregation strategies supported balanced optimization, and learning-rate modulation reduced divergence.
  • An average accuracy of 92.8% was achieved, with faster convergence and reduced performance variability.

Conclusions:

  • The proposed IFLF effectively addresses federated learning challenges posed by data heterogeneity.
  • The framework improves training stability, accuracy, and transparency in distributed machine learning.