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Related Experiment Video

Updated: Sep 9, 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

DA-PFL: Dynamic Affinity Aggregation in Personalized Federated Learning Under Class Imbalance.

Xu Yang, Jiyuan Feng, Yongxin Tong

IEEE Transactions on Neural Networks and Learning Systems

|September 3, 2025

Summary

This summary is machine-generated.

This study introduces a dynamic affinity-based personalized federated learning (PFL) model to address class imbalance. The novel approach improves model accuracy for individual clients in federated learning scenarios.

Related Experiment Videos

Last Updated: Sep 9, 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

Area of Science:

Artificial Intelligence
Machine Learning
Distributed Systems

Background:

Personalized federated learning (PFL) aims to create tailored models for each client.
Current PFL methods often aggregate clients with similar data distributions.
This similarity-based aggregation can worsen the class imbalance problem in datasets.

Purpose of the Study:

To propose a novel dynamic affinity-based PFL (DA-PFL) model.
To alleviate the class imbalance issue inherent in federated learning.
To enhance the performance of personalized learning models.

Main Methods:

Developed a complementary affinity metric to guide client aggregation.
Implemented a dynamic aggregation strategy adjusting client selection each round.
Evaluated the DA-PFL model on four real-world datasets.

Main Results:

The DA-PFL model significantly improved client accuracy.
Effectively reduced the negative impact of class imbalance during federated learning.
Outperformed existing state-of-the-art comparison methods.

Conclusions:

The proposed DA-PFL model offers an effective solution for class imbalance in PFL.
Dynamic aggregation based on affinity enhances personalized model performance.
DA-PFL demonstrates superior accuracy across diverse datasets.