Federated Prototype Graph Learning
Journal:
arXiv
Published Date:
Apr 13, 2025
Abstract
In recent years, Federated Graph Learning (FGL) has gained significant
attention for its distributed training capabilities in graph-based machine
intelligence applications, mitigating data silos while offering a new
perspective for privacy-preserve large-scale graph learning. However,
multi-level FGL heterogeneity presents various client-server collaboration
challenges: (1) Model-level: The variation in clients for expected performance
and scalability necessitates the deployment of heterogeneous models.
Unfortunately, most FGL methods rigidly demand identical client models due to
the direct model weight aggregation on the server. (2) Data-level: The
intricate nature of graphs, marked by the entanglement of node profiles and
topology, poses an optimization dilemma. This implies that models obtained by
federated training struggle to achieve superior performance. (3)
Communication-level: Some FGL methods attempt to increase message sharing among
clients or between clients and the server to improve training, which inevitably
leads to high communication costs. In this paper, we propose FedPG as a general
prototype-guided optimization method for the above multi-level FGL
heterogeneity. Specifically, on the client side, we integrate multi-level
topology-aware prototypes to capture local graph semantics. Subsequently, on
the server side, leveraging the uploaded prototypes, we employ topology-guided
contrastive learning and personalized technology to tailor global prototypes
for each client, broadcasting them to improve local training. Experiments
demonstrate that FedPG outperforms SOTA baselines by an average of 3.57\% in
accuracy while reducing communication costs by 168x.
