Cluster-driven Graph Federated Learning over Multiple Domains (2104.14628v1)

Abstract: Federated Learning (FL) deals with learning a central model (i.e. the server) in privacy-constrained scenarios, where data are stored on multiple devices (i.e. the clients). The central model has no direct access to the data, but only to the updates of the parameters computed locally by each client. This raises a problem, known as statistical heterogeneity, because the clients may have different data distributions (i.e. domains). This is only partly alleviated by clustering the clients. Clustering may reduce heterogeneity by identifying the domains, but it deprives each cluster model of the data and supervision of others. Here we propose a novel Cluster-driven Graph Federated Learning (FedCG). In FedCG, clustering serves to address statistical heterogeneity, while Graph Convolutional Networks (GCNs) enable sharing knowledge across them. FedCG: i) identifies the domains via an FL-compliant clustering and instantiates domain-specific modules (residual branches) for each domain; ii) connects the domain-specific modules through a GCN at training to learn the interactions among domains and share knowledge; and iii) learns to cluster unsupervised via teacher-student classifier-training iterations and to address novel unseen test domains via their domain soft-assignment scores. Thanks to the unique interplay of GCN over clusters, FedCG achieves the state-of-the-art on multiple FL benchmarks.

• The paper presents FedCG, a framework that uses iterative clustering and GCNs to address statistical heterogeneity in federated learning, achieving 3-8% accuracy improvements over baselines.
• The methodology employs a teacher-student classifier for unsupervised client data clustering and integrates domain-specific residual branches to adapt the central model to diverse data distributions.
• Graph convolutional networks dynamically model domain interactions, fostering effective knowledge sharing among domains and enhancing overall model generalization while preserving privacy.

Cluster-driven Graph Federated Learning Over Multiple Domains

The paper "Cluster-driven Graph Federated Learning over Multiple Domains" introduces a novel framework named Cluster-driven Graph Federated Learning (FedCG). FedCG is designed to address statistical heterogeneity in Federated Learning (FL) scenarios by leveraging clustering and Graph Convolutional Networks (GCNs). It seeks to overcome challenges arising from non-i.i.d. and unbalanced data distributions across different clients, a common complication in FL systems.

Introduction

Federated Learning is a decentralized form of machine learning where a central model (server) is trained using data stored on multiple client devices. This approach is inherently privacy-preserving because the server does not have direct access to raw data. However, this central model typically assumes data from different clients are identically distributed, an assumption often violated in real-world applications due to the diversity of data across clients. Statistical heterogeneity significantly affects the learning process, necessitating advanced strategies to effectively combine client updates.

Proposed Methodology

FedCG incorporates several innovative aspects:

1. Domain Identification via Clustering: FedCG begins with the identification of domains from client data using an iterative clustering process compliant with FL requirements. The process employs a teacher-student classifier model to perform unsupervised clustering of client data without compromising privacy.
2. Domain-specific Modules: The model introduces domain-specific components—residual branches—that allow customization of the central model to different data distributions. These components are tailored for the identified domains and integrated into the main model.
3. Graph-based Domain Interaction: Through GCNs, FedCG captures interactions among domain-specific components, facilitating the sharing of knowledge across different domains. The adjacency matrix in GCNs is dynamically populated using the inverse pairwise distances of domain-specific parameters, thereby modeling domain similarities directly in the parameter space.

FedCG showcases improved performance due to its sophisticated use of GCNs, which enhance learning by ensuring domain components interact based on domain similarities.

Results

FedCG demonstrates state-of-the-art performance on multiple FL benchmarks, outperforming traditional model aggregation techniques such as Federated Averaging (FedAvg) and proving superior to other methodologies focused on handling statistical heterogeneity, such as FedProx and SCAFFOLD. In the experimentation phase, FedCG achieved remarkable accuracy improvements of 3% to 8% over baseline models, depending on the dataset complexity.

Implications and Future Directions

The introduction of FedCG presents several implications for both theoretical exploration and practical deployment of FL systems:

• Improved Generalization Across Domains: By integrating domain-specific residuals and leveraging GCN, FedCG enhances the ability of FL models to generalize across diverse datasets and adapt to unseen data distributions.
• Privacy Preservation: The clustering technique respects client privacy, crucial for applications in sensitive domains such as healthcare and finance.
• Scalability: The reliance on clustering and graph-based learning supports scalability to numerous clients with diverse data scenarios, potentially revolutionizing distributed machine learning systems.

Future research can investigate alternative graph-based strategies or enhance domain adaptation techniques in FL frameworks to further improve learning consistency across unseen domains. Exploring hybrid approaches combining FedCG with other federated personalization techniques may provide new pathways for optimizing federated systems.

In conclusion, this paper contributes significantly to the field of federated learning by addressing statistical heterogeneity and improving model adaptability and performance across various data domains without compromising privacy.