  Federated Graph Learning (FGL) has garnered widespread attention by enabling
collaborative training on multiple clients for semi-supervised classification
tasks. However, most existing FGL studies do not well consider the missing
inter-client topology information in real-world scenarios, causing insufficient
feature aggregation of multi-hop neighbor clients during model training.
Moreover, the classic FGL commonly adopts the FedAvg but neglects the high
training costs when the number of clients expands, resulting in the overload of
a single edge server. To address these important challenges, we propose a novel
FGL framework, named SpreadFGL, to promote the information flow in edge-client
collaboration and extract more generalized potential relationships between
clients. In SpreadFGL, an adaptive graph imputation generator incorporated with
a versatile assessor is first designed to exploit the potential links between
subgraphs, without sharing raw data. Next, a new negative sampling mechanism is
developed to make SpreadFGL concentrate on more refined information in
downstream tasks. To facilitate load balancing at the edge layer, SpreadFGL
follows a distributed training manner that enables fast model convergence.
Using real-world testbed and benchmark graph datasets, extensive experiments
demonstrate the effectiveness of the proposed SpreadFGL. The results show that
SpreadFGL achieves higher accuracy and faster convergence against
state-of-the-art algorithms.
