FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks (2104.07145v2)

Abstract: Graph Neural Network (GNN) research is rapidly growing thanks to the capacity of GNNs in learning distributed representations from graph-structured data. However, centralizing a massive amount of real-world graph data for GNN training is prohibitive due to privacy concerns, regulation restrictions, and commercial competitions. Federated learning (FL), a trending distributed learning paradigm, provides possibilities to solve this challenge while preserving data privacy. Despite recent advances in vision and language domains, there is no suitable platform for the FL of GNNs. To this end, we introduce FedGraphNN, an open FL benchmark system that can facilitate research on federated GNNs. FedGraphNN is built on a unified formulation of graph FL and contains a wide range of datasets from different domains, popular GNN models, and FL algorithms, with secure and efficient system support. Particularly for the datasets, we collect, preprocess, and partition 36 datasets from 7 domains, including both publicly available ones and specifically obtained ones such as hERG and Tencent. Our empirical analysis showcases the utility of our benchmark system, while exposing significant challenges in graph FL: federated GNNs perform worse in most datasets with a non-IID split than centralized GNNs; the GNN model that attains the best result in the centralized setting may not maintain its advantage in the FL setting. These results imply that more research efforts are needed to unravel the mystery behind federated GNNs. Moreover, our system performance analysis demonstrates that the FedGraphNN system is computationally efficient and secure to large-scale graphs datasets. We maintain the source code at https://github.com/FedML-AI/FedGraphNN.

• The paper introduces FedGraphNN, a comprehensive framework that benchmarks GNNs across graph-, subgraph-, and node-level federated settings.
• It details a unified system integrating 36 diverse datasets to simulate real-world, privacy-preserving, non-IID data environments.
• Empirical results show that federated GNNs underperform centralized models, highlighting challenges and opportunities for advancing FL algorithms.

Federated Learning for Graph Neural Networks: Analyzing FedGraphNN

The research paper "FedGraphNN: A Federated Learning Benchmark System for Graph Neural Networks" presents a comprehensive framework and benchmark system designed for the emerging domain of federated learning (FL) applied to Graph Neural Networks (GNNs). In the era of growing concerns around data privacy, the application of FL to GNNs addresses the critical issue of decentralized data spread across various domains and industries. The paper introduces FedGraphNN as a cohesive platform that facilitates experimentation, evaluation, and enhancement of federated GNN models.

Federated learning is particularly relevant for GNNs due to the inherent structure and distribution of graph data across different data silos. In traditional machine learning, data centralization is often infeasible due to regulatory, privacy, and competitive reasons. This scenario is exacerbated in graph-based applications such as drug discovery, social networking, recommendation systems, and traffic flow modeling, where data is inherently decentralized. The paper provides three distinct settings for graph FL—graph-level, subgraph-level, and node-level—thus accommodating various real-world data distributions.

FedGraphNN is structured as a federated learning benchmark that integrates an array of datasets, GNN architectures, and FL algorithms within a secure and efficient system. The paper's introduction clarifies that FedGraphNN is built upon a unified formulation of graph FL, enhancing consistency across assessments. The benchmark includes a diverse range of datasets, 36 in total, from multiple domains like molecular research, bioinformatics, and social computing, facilitating wide applicability. These datasets are partitioned in a privacy-preserving manner to reflect realistic non-IID conditions typical in federated settings.

The empirical findings of the paper indicate notable challenges in the FL domain when applied to GNNs. Performance degradation in federated settings compared to centralized training underscores the complexity introduced by non-IID data partitions. For instance, findings demonstrate that "federated GNNs perform worse in most datasets with a non-IID split than centralized GNNs," highlighting the necessity for further exploration into the underpinnings of these results. This disparity signals an opportunity and a need to develop advanced FL algorithms capable of addressing the unique dynamics of graph data in federated systems.

From a theoretical and practical standpoint, the FedGraphNN system offers numerous advancements for researchers. It supports the implementation and comparison of popular GNN models, such as GCN, GAT, and GraphSAGE, incorporated with federated learning strategies like FedAvg. Additionally, the system includes high-level APIs and deployment capabilities, which facilitate easy integration and experimentation in diverse computational environments.

The paper also addresses the aspect of security through the integration of secure aggregation techniques. This ensures that the risk of privacy breaches in federated environments is minimized, a critical consideration for industrial application of such technologies.

Future directions for research, as anticipated by the authors, include expanding the range of datasets and models, optimizing system efficiency, and tackling the omnipresent challenge of data heterogeneity in graph FL. The paper also suggests pursuing label-efficient and self-supervised learning models to improve the effectiveness of federated GNNs in instances where labeled data is sparse or entirely absent.

In conclusion, "FedGraphNN: A Federated Learning Benchmark System for Graph Neural Networks" establishes a significant stepping stone for the intersection of federated learning and graph neural networks. It presents both a methodological framework and a practical toolkit for advancing this emerging field. Through further enhancement and wider adoption, FedGraphNN can substantively contribute to the privacy-preserving analysis of complex graph data distributed across disparate sources. It lays the groundwork for subsequent research efforts and offers a robust infrastructure for exploring the nuanced challenges presented by federated learning applied to GNNs.