FedFixer: Mitigating Heterogeneous Label Noise in Federated Learning

Grunnleggende konsepter

Proposing FedFixer to address heterogeneous label noise challenges in Federated Learning.

Sammendrag

Abstract:

FL relies on label quality but faces noisy and heterogeneous labels.
FedFixer introduces a personalized model to select clean client-specific samples effectively.

Introduction:

FL struggles with heterogeneous label noise, impacting generalization performance.
Existing methods face challenges due to limited sample sizes and struggle with noisy data.

Dual Model Structure Approach:

FedFixer uses global and personalized models for effective learning in the presence of label noise.
Regularizers like Confidence Regularizer and Distance Regularizer mitigate overfitting issues.

Experiments:

Conducted experiments on benchmark datasets showcasing FedFixer's effectiveness in filtering noisy labels.

Comparison with State-of-the-Art Methods:

FedFixer outperforms other methods across different settings, especially in highly noisy scenarios.

Ablation Study:

Components like Confidence Regularizer, Distance Regularizer, Alternate Updates, and Personalized Model contribute to performance improvement.

Statistikk

ベンチマークデータセットでの実験により、FedFixerはノイズラベルを効果的にフィルタリングすることが示されました。

Sitater

"FedFixer can perform well in filtering noisy label samples on different clients."
"Existing methods designed for the issue of label noise in FL can be broadly categorized into coarse-grained and fine-grained methods."

Viktige innsikter hentet fra

FedFixer

by Xinyuan Ji,Z... klokken arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16561.pdf

Dypere Spørsmål

How can the concept of FedFixer be applied to other machine learning algorithms beyond Federated Learning

FedFixer's concept can be applied to other machine learning algorithms by incorporating a dual model structure that focuses on personalized and global models. This approach can help in scenarios where the data is distributed across multiple sources, and there is a need to filter out noisy or incorrect labels. By implementing a mechanism for selecting clean client-specific samples and updating models alternately, similar to FedFixer, other machine learning algorithms can improve their performance in handling heterogeneous label noise.

What are potential drawbacks or limitations of using a dual model structure approach like FedFixer

One potential drawback of using a dual model structure approach like FedFixer is the increased complexity it introduces into the system. Managing two separate models (personalized and global) along with regularizers may require additional computational resources and expertise for implementation and maintenance. Additionally, there could be challenges in fine-tuning hyperparameters related to the regularizers or balancing updates between the two models effectively. Moreover, if not properly designed or implemented, there might be issues with overfitting on local data due to limited sample sizes.

How might the principles behind FedFixer be adapted for applications outside of machine learning

The principles behind FedFixer can be adapted for applications outside of machine learning by considering similar strategies for addressing noise or inconsistencies in various processes or systems. For example:

In data processing: Dual model structures could be used to filter out erroneous data entries before analysis.
In quality control: Implementing personalized checks alongside standard procedures could enhance error detection.
In financial auditing: Utilizing global standards while allowing for customized approaches based on specific contexts could improve accuracy.
By applying the concepts of personalized filtering and global aggregation from FedFixer in different domains, organizations can enhance decision-making processes by reducing errors caused by noisy inputs or inconsistent information.