FedDyH: A Multi-Policy with GA Optimization Framework for Dynamic Heterogeneous Federated Learning | JoVE Visualize

Area of Science:

Artificial Intelligence
Machine Learning
Computational Biology

Background:

Federated learning (FL) enables privacy-preserving distributed learning, particularly for cross-institutional medical image analysis.
Real-world data exhibit dynamic heterogeneity (e.g., disease progression, imaging device variations), causing catastrophic forgetting and performance degradation in FL models.
Existing FL methods inadequately address dynamic heterogeneity, limiting their effectiveness in complex, evolving medical datasets.

Purpose of the Study:

To propose the FedDyH framework, an innovative solution designed to tackle dynamic data heterogeneity challenges in federated learning.
To enhance the robustness and accuracy of federated learning models in dynamic, heterogeneous environments by drawing inspiration from biological adaptive regulation.

Main Methods:

The FedDyH framework employs cross-client knowledge distillation to simulate intercellular information transfer, preserving local features and mitigating knowledge forgetting.
A dynamic regularization term adaptively adjusts its strength, mimicking regulatory T cells to balance global convergence with local specificity.
A genetic algorithm (GA) is integrated for adaptive hyperparameter optimization, simulating biological evolution for improved model adaptability and performance.

Main Results:

The FedDyH framework demonstrated significant accuracy improvements over the SOTA baseline FedDecorr on benchmark datasets: MNIST (+2.59%), Fashion-MNIST (+0.55%), and CIFAR-10 (+5.79%).
Experimental results validate the framework's effectiveness in addressing data heterogeneity in dynamic environments.
The study highlights the novelty of using optimization algorithms like GA for hyperparameter tuning in federated learning.

Conclusions:

The FedDyH framework offers a robust and adaptive solution for federated learning in dynamic, heterogeneous environments, crucial for medical image analysis.
The biologically inspired approach enhances model stability and predictive accuracy by effectively managing data variations.
This work advances federated learning by introducing novel methods for handling dynamic heterogeneity and optimizing model performance.