Personalized Federated Learning via Learning Dynamic Graphs (2503.05474v1)

Abstract: Personalized Federated Learning (PFL) aims to train a personalized model for each client that is tailored to its local data distribution, learning fails to perform well on individual clients due to variations in their local data distributions. Most existing PFL methods focus on personalizing the aggregated global model for each client, neglecting the fundamental aspect of federated learning: the regulation of how client models are aggregated. Additionally, almost all of them overlook the graph structure formed by clients in federated learning. In this paper, we propose a novel method, Personalized Federated Learning with Graph Attention Network (pFedGAT), which captures the latent graph structure between clients and dynamically determines the importance of other clients for each client, enabling fine-grained control over the aggregation process. We evaluate pFedGAT across multiple data distribution scenarios, comparing it with twelve state of the art methods on three datasets: Fashion MNIST, CIFAR-10, and CIFAR-100, and find that it consistently performs well.

Personalized Federated Learning via Learning Dynamic Graphs: An Analytical Overview

This paper introduces a novel approach to Personalized Federated Learning (PFL) named pFedGAT, which employs Graph Attention Networks (GAT) to dynamically model the connections between client nodes in federated learning scenarios. The motivation behind this work arises from the limitations of traditional federated learning, especially in non-IID data scenarios, where global models often fail to perform well due to the diverse data distributions across clients.

Methodology

pFedGAT distinguishes itself from prior PFL methods by focusing on enhancing the collaborative relationships among clients through dynamic graph learning. The process begins by representing each client as a node in a graph, with node features constructed from model parameters. The use of GAT allows the framework to capture fine-grained dependencies between clients and adjust aggregation weights dynamically. This capability is pivotal in tailoring personalized models that can accurately reflect the unique data distributions at each client.

Numerical Results

The proposed method is rigorously tested across multiple datasets such as Fashion MNIST, CIFAR-10, and CIFAR-100 under different heterogeneity levels—homogeneous (IID), pathological, and Dirichlet distributions. The empirical findings demonstrate that pFedGAT consistently performs well, either outperforming or matching the state-of-the-art methods in terms of model accuracy across varying scenarios. Notably, by averaging performance across scenarios, pFedGAT achieves superior results, underscoring its robustness in handling diverse client data distributions.

Implications and Future Directions

The implications of this research are multifaceted. Practically, pFedGAT offers a scalable solution for real-world applications where data privacy and personalization are paramount, such as healthcare and mobile computing. Theoretically, it contributes to the body of knowledge in federated learning by proposing a sophisticated mechanism for model aggregation—challenging the notion that federated learning needs to rely solely on static aggregation strategies.

Looking ahead, there are several promising avenues for further research. The adaptability of GAT in dynamically adjusting aggregation weights can be explored for other applications, like real-time data streams or cross-domain tasks. Furthermore, integrating differential privacy mechanisms within this framework can enhance data security while maintaining personalization.

In conclusion, pFedGAT represents a significant step forward in the personalized federated learning space. Its innovative use of GAT for dynamic graph modeling offers an adaptable solution to address the distributional heterogeneity among clients, and experimental validations firmly establish its efficacy across multiple scenarios. This work lays a strong foundation for future explorations in deploying federated learning in complex real-world environments while safeguarding individual data privacy.