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Related Experiment Video

Updated: Aug 1, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Privacy preserving federated learning for full heterogeneity.

Kongyang Chen¹, Xiaoxue Zhang², Xiuhua Zhou²

¹Institute of Artificial Intelligence and Blockchain, Guangzhou University, China; Pazhou Lab, Guangzhou, China; Jiangsu Key Laboratory of Media Design and Software Technology, Jiangnan University, Wuxi, China.

ISA Transactions

|April 27, 2023

Summary

Full Heterogeneous Federated Learning (FHFL) addresses data, model, and computation challenges in federated learning. Our novel FHFL method enhances global model performance by tackling these issues simultaneously.

Keywords:

Federated learning Knowledge distillation Non-IID Privacy preserving

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Area of Science:

Machine Learning
Distributed Systems
Data Science

Background:

Federated learning enables collaborative model training while preserving data privacy.
Practical federated learning faces challenges like data, model, and computation heterogeneity, degrading performance.
Existing solutions inadequately address multiple heterogeneity challenges simultaneously.

Purpose of the Study:

To introduce Full Heterogeneous Federated Learning (FHFL), a novel approach.
To simultaneously address data, model, and computation heterogeneity in federated learning.
To improve global model performance in diverse federated learning environments.

Main Methods:

Synthetic data generation to mitigate Non-IID data heterogeneity.
Knowledge distillation for aggregating insights from heterogeneous client models.
Opportunistic computation scheduling to leverage idle resources for faster clients.

Main Results:

FHFL demonstrates excellent model training performance across various datasets.
The proposed methods effectively address multiple heterogeneity challenges.
Significant improvements in global model performance were observed.

Conclusions:

FHFL offers a pioneering solution for distributed model training among heterogeneous clients.
The approach effectively tackles the complex interplay of heterogeneity in federated learning.
FHFL paves the way for more robust and efficient federated learning systems.