FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning (2204.05562v5)

Abstract: The incredible development of federated learning (FL) has benefited various tasks in the domains of computer vision and natural language processing, and the existing frameworks such as TFF and FATE has made the deployment easy in real-world applications. However, federated graph learning (FGL), even though graph data are prevalent, has not been well supported due to its unique characteristics and requirements. The lack of FGL-related framework increases the efforts for accomplishing reproducible research and deploying in real-world applications. Motivated by such strong demand, in this paper, we first discuss the challenges in creating an easy-to-use FGL package and accordingly present our implemented package FederatedScope-GNN (FS-G), which provides (1) a unified view for modularizing and expressing FGL algorithms; (2) comprehensive DataZoo and ModelZoo for out-of-the-box FGL capability; (3) an efficient model auto-tuning component; and (4) off-the-shelf privacy attack and defense abilities. We validate the effectiveness of FS-G by conducting extensive experiments, which simultaneously gains many valuable insights about FGL for the community. Moreover, we employ FS-G to serve the FGL application in real-world E-commerce scenarios, where the attained improvements indicate great potential business benefits. We publicly release FS-G, as submodules of FederatedScope, at https://github.com/alibaba/FederatedScope to promote FGL's research and enable broad applications that would otherwise be infeasible due to the lack of a dedicated package.

• The paper presents FederatedScope-GNN, a unified framework that streamlines federated graph learning through modular design and automated tuning.
• The package offers a comprehensive repository of graph datasets and models, enabling effective benchmarking and deployment of FGL algorithms.
• Experimental results demonstrate improved performance in real-world applications while incorporating strong privacy defenses.

FederatedScope-GNN: Advancements in Federated Graph Learning

The paper "FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning" presents FederatedScope-GNN (FS-G), a package designed to address the unique challenges in federated graph learning (FGL). This paper identifies the need for a dedicated framework that accommodates the specific characteristics of graph data, absent in traditional federated learning (FL) frameworks like TensorFlow Federated and FATE.

Key Features

FS-G is built to streamline the process of developing, benchmarking, and deploying FGL algorithms. The paper highlights several innovative features of the FS-G package:

1. Unified Framework for FGL: FS-G offers a coherent approach to express FGL algorithms through a modular architecture. It provides extensive support for diverse data exchanges and interactions specific to FGL, unlike traditional FL frameworks.
2. Comprehensive DataZoo and ModelZoo: The package includes a vast repository of graph datasets and models, facilitating ready-to-use capabilities for FGL. This feature ensures that researchers can quickly implement and compare different FGL methodologies.
3. Efficient Model Auto-tuning: FS-G incorporates an automated model tuning component tailored for hyper-parameter optimization (HPO) in FGL, addressing the high computational cost associated with federated training.
4. Privacy Features: The package also includes extensive privacy attack and defense strategies, crucial for maintaining data privacy in federated settings.

Experimental Validation

The paper reports the effectiveness of FS-G through extensive experiments that enhance understanding of FGL characteristics. In particular, FS-G is shown to improve performance in real-world E-commerce scenarios compared to locally trained models, indicating significant business potentials.

Implications and Future Directions

FS-G's contribution not only lies in providing a robust tool for practitioners but also in laying down a foundation for future FGL research. By releasing FS-G as an open-source package, the authors encourage widespread adoption, fostering innovation in leveraging graph data in federated settings. This approach is likely to accelerate advancements in privacy-preserving ML methodologies and broader applications of graph neural networks.

In conclusion, FederatedScope-GNN addresses critical gaps in the current FGL landscape, offering a comprehensive and effective framework for both research and industrial applications. As interest in federated learning continues to grow, tools like FS-G will play a pivotal role in progressing state-of-the-art solutions that balance the demands of data privacy and collaborative learning.