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Related Experiment Video

Updated: May 25, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

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Replica tree-based federated learning using limited data.

Ramona Ghilea¹, Islem Rekik¹

¹BASIRA Lab, Imperial-X (I-X) and Department of Computing, Imperial College London, London, UK.

Neural Networks : the Official Journal of the International Neural Network Society

|February 27, 2025

Summary

This summary is machine-generated.

RepTreeFL enhances federated learning for limited data and clients by creating diverse model replicas. This novel approach aggregates these replicas using a tree structure, improving performance in data-scarce scenarios.

Keywords:

Diversity Federated learning Limited data Replica

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Area of Science:

Machine Learning
Federated Learning
Artificial Intelligence

Background:

Deep neural networks require large datasets for optimal performance.
Centralized training strategies are well-established, but federated learning with limited data and clients is underexplored.
Real-world applications, particularly in healthcare, often involve constrained numbers of participating clients and limited data.

Purpose of the Study:

To propose a novel federated learning framework, RepTreeFL, designed for scenarios with limited data and a small number of clients.
To address the challenge of learning effectively when both data volume and client participation are constrained.
To enable robust model training in resource-limited federated environments.

Main Methods:

Introduced RepTreeFL, a federated learning framework utilizing client model replicas.
Replicated clients by copying model architecture and perturbing local data distributions to create model diversity.
Implemented a diversity-based tree aggregation strategy, dynamically updating weights based on model discrepancy.
Leveraged hierarchical client network structures (original and virtual) and model diversity for aggregation.

Main Results:

Demonstrated the effectiveness of RepTreeFL in learning from limited data and a small number of clients.
Showcased superior performance of RepTreeFL compared to existing methods in constrained settings.
Validated the framework across diverse tasks (graph generation, image classification) and data types (binary, multi-class).
Confirmed effectiveness with both homogeneous and heterogeneous model architectures.

Conclusions:

RepTreeFL successfully enables effective federated learning with limited data and clients.
The replica concept and diversity-based tree aggregation are key to overcoming data and client constraints.
The proposed framework offers a promising solution for practical federated learning applications in resource-limited environments.