FEDSTRUCT: Federated Decoupled Learning over Interconnected Graphs

Core Concepts

Decoupling node features and leveraging structural dependencies in federated learning on interconnected graphs.

Abstract

「FEDSTRUCT」は、グラフ上のフェデレーテッドラーニングにおいて、ノードの特徴を分離し、構造的依存関係を活用する革新的なフレームワークです。このアプローチは、プライバシー保護を向上させつつ、半教師あり学習や異種性グラフにも優れたパフォーマンスを発揮します。実験結果では、中央集権的な手法と比較して、「FEDSTRUCT」が近い性能を達成しました。また、異なるデータ分割方法やクライアント数に対しても堅牢性を示しました。

Stats

FEDSAGE+: 68.42±1.73
FEDSAGE-TRUE (CORA, 10 clients): 80.85±1.55
FEDSTRUCT (GDV, AMAZON RATINGS, 20 clients): 86.01±0.49

Quotes

"Unlike existing approaches, FEDSTRUCT eliminates the need for sharing sensitive node features or embeddings."
"We address the problem of subgraph FL for node classification within a global graph containing multiple, non-overlapping subgraphs belonging to different clients."
"FEDSTRUCT decouples graph structure and node feature information."

Key Insights Distilled From

FedStruct

by Java... at arxiv.org 03-01-2024

https://arxiv.org/pdf/2402.19163.pdf

Deeper Inquiries

How does FEDSTRUCT ensure privacy while sharing information among clients

FEDSTRUCT ensures privacy while sharing information among clients by decoupling node and structure features. Unlike other federated learning frameworks that require sharing sensitive node features or embeddings, FEDSTRUCT eliminates this need. Instead of exchanging sensitive information, FEDSTRUCT leverages global graph structure information to capture inter-node dependencies among clients. This approach safeguards data privacy by minimizing the exchange of potentially sensitive data between clients.

What are the implications of utilizing task-dependent node structure feature vectors like HOP2VEC

HOP2VEC introduces task-dependent node structure feature vectors in FEDSTRUCT. These NSFs are designed to be specific to the classification task at hand, capturing structural information about nodes beyond direct neighbors without requiring knowledge of the global graph connections. By generating NSFs that are tailored to minimize misclassification during training, HOP2VEC enhances the distinctiveness of nodes and facilitates more accurate node classification.

How does FEDSTRUCT compare to other federated learning methods in terms of scalability and performance

In terms of scalability and performance, FEDSTRUCT outperforms other federated learning methods due to its ability to handle heterophilic graphs effectively while ensuring privacy. The framework showcases excellent performance close to a centralized approach across various scenarios with different data partitioning methods, levels of label availability, and number of clients. Additionally, FEDSTRUCT demonstrates robustness across different partitionings and varying degrees of heterophily in datasets compared to existing FL methods like FL GNN and FEDSAGE+. Its utilization of deep structural dependencies allows for improved accuracy in semi-supervised learning settings with limited labeled training nodes as well as heavily heterophilic datasets.