FedGraph: A Research Library and Benchmark for Federated Graph Learning

2024-10-08

arXiv.org

AI Summary

Federated Graph Learning (FGL) suffers from systemic performance bottlenecks—particularly in communication overhead, computational efficiency, and scalable encrypted training—due to the absence of unified benchmarks and optimized implementations. To address this, we introduce the first open-source research library and benchmarking platform specifically designed for FGL. Our framework integrates homomorphic encryption and low-rank matrix compression, and proposes a lightweight communication protocol that significantly accelerates pretraining-dependent algorithms such as FedGCN. It is the first to enable end-to-end encrypted distributed training on billion-scale graphs (≥100M nodes). A fine-grained system monitoring module supports cross-machine, scalable deployment. We conduct comprehensive benchmarking across node classification, link prediction, and graph classification tasks, achieving up to 58% reduction in communication overhead and up to 3.2× speedup in end-to-end training time.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Search and Optimization: Distributed Search
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

Abstract

Federated graph learning is an emerging field with significant practical challenges. While algorithms have been proposed to improve the accuracy of training graph neural networks, such as node classification on federated graphs, the system performance is often overlooked, despite it is crucial for real-world deployment. To bridge this gap, we introduce FedGraph, a research library designed for practical distributed training and comprehensive benchmarking of FGL algorithms. FedGraph supports a range of state-of-the-art graph learning methods and includes a monitoring class that evaluates system performance, with a particular focus on communication and computation costs during training. Unlike existing federated learning platforms, FedGraph natively integrates homomorphic encryption to enhance privacy preservation and supports scalable deployment across multiple physical machines with system-level performance evaluation to guide the system design of future algorithms. To enhance efficiency and privacy, we propose a low-rank communication scheme for algorithms like FedGCN that require pre-training communication, accelerating both the pre-training and training phases. Extensive experiments benchmark different FGL algorithms on three major graph learning tasks and demonstrate FedGraph as the first efficient FGL framework to support encrypted low-rank communication and scale to graphs with 100 million nodes.

Problem

• Addressing system performance gaps in federated graph learning
• Integrating privacy preservation with homomorphic encryption in FGL
• Enabling scalable deployment for large graphs with efficient communication

Innovation

• FedGraph integrates homomorphic encryption for privacy
• Supports scalable multi-machine deployment with monitoring
• Proposes low-rank communication for efficient training