Collaborative Federated Learning with Aggregation-Free Global Model Training for Handling Data Heterogeneity

Основні поняття

A novel aggregation-free federated learning algorithm, FedAF, that leverages collaborative data condensation and local-global knowledge matching to effectively handle cross-client data heterogeneity and improve global model performance.

Анотація

The paper introduces FedAF, a novel aggregation-free federated learning (FL) algorithm designed to address the challenge of data heterogeneity across clients.
Key highlights:

Traditional FL methods follow an aggregate-then-adapt framework, which can lead to client drift and performance degradation, especially in the presence of significant cross-client data heterogeneity.
FedAF adopts an aggregation-free paradigm, where clients first learn condensed data by leveraging peer knowledge, and the server subsequently trains the global model using the condensed data and soft labels received from the clients.
FedAF introduces a collaborative data condensation scheme, which employs Sliced Wasserstein Distance-based regularization to align the local knowledge distribution with the broader distribution across clients, enhancing the quality of the condensed data.
FedAF further incorporates a local-global knowledge matching scheme, enabling the server to utilize not only the condensed data but also the soft labels extracted from client data, thereby refining and stabilizing the global model training process.
Extensive experiments on various benchmark datasets demonstrate that FedAF consistently outperforms state-of-the-art FL algorithms in handling both label-skew and feature-skew data heterogeneity, leading to superior global model accuracy and faster convergence.

Статистика

The paper does not provide specific numerical data points or statistics. The key results are presented in the form of model accuracy and convergence performance comparisons.

Цитати

"FedAF inherently avoids the issue of client drift, enhances the quality of condensed data amid notable data heterogeneity, and improves the global model performance."
"Extensive numerical studies on several popular benchmark datasets show FedAF surpasses various state-of-the-art FL algorithms in handling label-skew and feature-skew data heterogeneity, leading to superior global model accuracy and faster convergence."

Ключові висновки, отримані з

An Aggregation-Free Federated Learning for Tackling Data Heterogeneity

by Yuan Wang,Hu... о arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.18962.pdf

Глибші Запити

How can the collaborative data condensation scheme be further improved to better capture the underlying data distribution across clients

To further enhance the collaborative data condensation scheme and better capture the underlying data distribution across clients, several improvements can be considered:

Dynamic Weighting: Introduce a dynamic weighting mechanism that assigns different weights to the data contributed by each client based on the similarity of their data distribution to the global distribution. This way, clients with data more representative of the overall distribution will have a higher influence on the condensed data.
Adaptive Feature Extraction: Implement adaptive feature extraction techniques that adjust the feature extraction process based on the data characteristics of each client. This can help in extracting more relevant and informative features for data condensation.
Cross-Domain Knowledge Transfer: Incorporate mechanisms for cross-domain knowledge transfer, allowing clients to learn from data distributions of other domains to improve the quality of condensed data. This can be achieved through techniques like domain adaptation or transfer learning.

What are the potential drawbacks or limitations of the local-global knowledge matching approach, and how can they be addressed

The local-global knowledge matching approach, while beneficial, may have certain drawbacks or limitations:

Overfitting: There is a risk of overfitting when incorporating soft labels extracted from client data, especially if the soft labels are noisy or not representative of the true underlying distribution. Regularization techniques can be applied to mitigate this risk.
Privacy Concerns: Sharing soft labels from client data with the server may raise privacy concerns, especially in sensitive applications. Implementing privacy-preserving techniques such as differential privacy or secure multi-party computation can address these concerns.
Scalability: As the number of clients increases, the computational complexity of matching local and global knowledge may become a bottleneck. Implementing efficient algorithms and distributed computing strategies can help improve scalability.

What other applications or domains beyond computer vision could benefit from the aggregation-free federated learning framework proposed in this work

The aggregation-free federated learning framework proposed in this work can benefit various applications and domains beyond computer vision, including:

Healthcare: In healthcare, where data privacy is crucial, the framework can be applied to train models on distributed medical data from different hospitals without sharing sensitive patient information. This can enable the development of robust and accurate healthcare AI models.
Finance: In the financial sector, where regulatory constraints limit data sharing, the framework can facilitate collaborative model training on financial data from different institutions while maintaining data privacy and security.
IoT: In the Internet of Things (IoT) domain, where data is generated and stored across various devices, the framework can support collaborative learning on decentralized IoT data streams, enabling the development of intelligent IoT applications without centralizing data.
Telecommunications: In telecommunications, the framework can be used to train models on data from different network nodes or devices, improving network optimization, anomaly detection, and predictive maintenance without compromising data privacy.