Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition
Journal:
arXiv
Published Date:
Dec 18, 2024
Abstract
Federated graph learning (FGL) has gained significant attention for enabling
heterogeneous clients to process their private graph data locally while
interacting with a centralized server, thus maintaining privacy. However, graph
data on clients are typically non-IID, posing a challenge for a single model to
perform well across all clients. Another major bottleneck of FGL is the high
cost of communication. To address these challenges, we propose a
communication-efficient personalized federated graph learning algorithm, CEFGL.
Our method decomposes the model parameters into low-rank generic and sparse
private models. We employ a dual-channel encoder to learn sparse local
knowledge in a personalized manner and low-rank global knowledge in a shared
manner. Additionally, we perform multiple local stochastic gradient descent
iterations between communication phases and integrate efficient compression
techniques into the algorithm. The advantage of CEFGL lies in its ability to
capture common and individual knowledge more precisely. By utilizing low-rank
and sparse parameters along with compression techniques, CEFGL significantly
reduces communication complexity. Extensive experiments demonstrate that our
method achieves optimal classification accuracy in a variety of heterogeneous
environments across sixteen datasets. Specifically, compared to the
state-of-the-art method FedStar, the proposed method (with GIN as the base
model) improves accuracy by 5.64\% on cross-datasets setting CHEM, reduces
communication bits by a factor of 18.58, and reduces the communication time by
a factor of 1.65.
