Mitigating Heterogeneous Label Noise in Federated Learning with FedFixer

Kernekoncepter

FedFixer proposes a dual model structure to address heterogeneous label noise in Federated Learning, achieving superior performance.

Resumé

Abstract:

FedFixer introduces a personalized model to filter noisy label samples effectively.
Dual models prevent error accumulation and overfitting.

Introduction:

FL faces challenges from heterogeneous label noise degrading generalization performance.
Existing methods struggle with noisy labels in FL due to limited sample sizes.

Data Extraction:

"The results demonstrate that FedFixer can perform well in filtering noisy label samples on different clients, especially in highly heterogeneous label noise scenarios."

Quotations:

"Federated Learning heavily depends on label quality for its performance."

Related Work:

Robust FL methods focus on client-level and sample-level approaches.

Experiments:

Extensive experiments validate FedFixer's effectiveness across benchmark datasets.

Ablation Study:

Components like Confidence Regularizer and Distance Regularizer contribute significantly to performance improvement.

Statistik

The results demonstrate that FedFixer can perform well in filtering noisy label samples on different clients, especially in highly heterogeneous label noise scenarios.

Citater

"Federated Learning heavily depends on label quality for its performance."

Vigtigste indsigter udtrukket fra

FedFixer

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

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

Dybere Forespørgsler

How does the personalized model impact the overall performance of the dual model structure

The personalized model plays a crucial role in the overall performance of the dual model structure in Federated Learning. By introducing a personalized model, FedFixer aims to address the issue of heterogeneous label noise by learning client-specific samples effectively. The personalized model helps in distinguishing between clean and noisy label samples on individual clients, reducing the risk of misidentifying client-specific data as noisy labels. In the dual models, both the global model and personalized model work together to filter out mislabeled instances and improve the overall accuracy of the FL task.

What are the implications of overfitting on the local updates of the personalized model

Overfitting on local updates of the personalized model can have significant implications for Federated Learning algorithms like FedFixer. When updating solely based on local client data, without considering a broader perspective from other clients or incorporating regularization techniques, there is a high risk of overfitting due to limited sample sizes available at each client. This overfitting can lead to poor generalization capabilities and reduced performance not only on local models but also on global models trained through aggregation methods like FedAvg.

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

The concept of distance regularizer used in Federated Learning algorithms like FedFixer can be applied to other machine learning algorithms beyond FL that involve multiple models or components with shared parameters. For instance:

In transfer learning scenarios where pre-trained models are fine-tuned on new datasets, a distance regularizer can help control how much newly learned information deviates from existing knowledge.
Regularizing neural networks during training with adversarial examples could benefit from a distance regularizer to constrain changes made by adversarial perturbations.
Collaborative filtering systems in recommendation engines could use distance regularization between user-based and item-based collaborative filtering approaches to prevent one approach from dominating recommendations excessively.
By applying distance regularization techniques across various machine learning domains, it becomes possible to enhance robustness, prevent overfitting, and improve generalization capabilities within complex modeling architectures.