ICLR 2025

Published as a conference paper at ICLR 2025
DECOUPLED SUBGRAPH FEDERATED LEARNING
Javad Aliakbari1
Johan ¨Ostman2
Alexandre Graell i Amat1
1Chalmers University of Technology
2AI Sweden
ABSTRACT
We address the challenge of federated learning on graph-structured data distributed
across multiple clients. Specifically, we focus on the prevalent scenario of in-
terconnected subgraphs, where interconnections between different clients play a
critical role. We present a novel framework for this scenario, named FEDSTRUCT,
that harnesses deep structural dependencies. To uphold privacy, unlike existing
methods, FEDSTRUCT eliminates the necessity of sharing or generating sensitive
node features or embeddings among clients. Instead, it leverages explicit global
graph structure information to capture inter-node dependencies. We validate the ef-
fectiveness of FEDSTRUCT through experimental results conducted on six datasets
for semi-supervised node classification, showcasing performance close to the cen-
tralized approach across various scenarios, including different data partitioning
methods, varying levels of label availability, and number of clients.
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INTRODUCTION
Many real-world data are graph-structured, where nodes represent entities and edges capture their re-
lationships. For example, in anti-money laundering, nodes symbolize accounts and edges correspond
to confidential transactions.
Graph neural networks (GNNs) are specialized neural networks for graph-structured data, showing
success in fields such as drug discovery, social networks, or traffic flow modeling (Stokes et al.,
2020; Fan et al., 2019; Jiang and Luo, 2022). In many practical applications, graph data is inherently
distributed across multiple clients, i.e., the global graph encompasses multiple, non-overlapping
subgraphs. For example, in anti-money laundering, local graphs represent internal transactional
networks of financial institutions.
For this and many other applications, data sharing among clients is ...