arxiv_ai 95% Match Research Paper ML Researchers,GNN Researchers,Federated Learning Experts,Privacy Engineers 1 week ago

Subgraph Federated Learning via Spectral Methods

graph-neural-networks › graph-learning

📄 Abstract

Abstract: We consider the problem of federated learning (FL) with graph-structured data distributed across multiple clients. In particular, we address the prevalent scenario of interconnected subgraphs, where interconnections between clients significantly influence the learning process. Existing approaches suffer from critical limitations, either requiring the exchange of sensitive node embeddings, thereby posing privacy risks, or relying on computationally-intensive steps, which hinders scalability. To tackle these challenges, we propose FedLap, a novel framework that leverages global structure information via Laplacian smoothing in the spectral domain to effectively capture inter-node dependencies while ensuring privacy and scalability. We provide a formal analysis of the privacy of FedLap, demonstrating that it preserves privacy. Notably, FedLap is the first subgraph FL scheme with strong privacy guarantees. Extensive experiments on benchmark datasets demonstrate that FedLap achieves competitive or superior utility compared to existing techniques.

Authors (4)

Javad Aliakbari

Johan Östman

Ashkan Panahi

Alexandre Graell i Amat

Submitted

October 29, 2025

arXiv Category

cs.LG

arXiv PDF

Key Contributions

FedLap is a novel framework for subgraph federated learning that addresses privacy and scalability concerns. It leverages global structure information via Laplacian smoothing in the spectral domain to capture inter-node dependencies while providing strong privacy guarantees, making it the first subgraph FL scheme with such guarantees.

Business Value

Enables secure and efficient collaborative learning on distributed graph data, crucial for applications in social networks, recommendation systems, and cybersecurity where data privacy is paramount.

Paper Metadata

Innovation Type

Algorithmic Framework

Deployment Feasibility

Moderate, requires distributed infrastructure and careful implementation of spectral methods.

Limitations Addressed

Existing subgraph FL methods either pose privacy risks by exchanging sensitive node embeddings or are computationally intensive, hindering scalability. FedLap overcomes these limitations.

Performance Gains

Achieves strong privacy guarantees and demonstrates effectiveness on benchmark datasets, outperforming existing methods in privacy and scalability.

Technical Tags

Federated Learning (FL)Graph Neural Networks (GNNs)Subgraph Federated LearningLaplacian smoothingspectral methodsprivacy preservationscalabilityinter-client connectionsnode embeddingsFedLap

Research Topics

Federated LearningGraph Neural NetworksPrivacy-Preserving Machine LearningDistributed SystemsGraph Representation Learning

Methods & Architectures

Laplacian Smoothing in Spectral DomainFedLap FrameworkPrivacy AnalysisExtensive Experiments Graph Neural Networks (GNNs)

Applications & Tasks

Distributed Machine Learning Graph Data Analysis Privacy-Preserving AI Privacy risks in subgraph FL (exchange of node embeddings)Computational intensity of existing subgraph FL methodsHandling interconnected subgraphs effectively Privacy-preserving subgraph federated learningEfficiently capturing inter-node dependenciesScalable FL for graph data

Related Fields

Machine LearningGraph TheoryDistributed SystemsPrivacyComputer Science

Keywords

federated learninggraph neural networkssubgraphprivacyscalabilityLaplacian smoothingspectral methodsdistributed learningFedLapgraph data

Academic Context

#Federated Learning#Graph Neural Networks#Privacy-Preserving Machine Learning#Distributed Systems#Graph Representation Learning

Commercial Potential

Potential Products

Privacy-preserving federated learning platforms for graph dataSecure graph analysis toolsDecentralized machine learning solutions

Target Industries

TechnologyFinanceHealthcareSocial MediaTelecommunications

Use Case Examples

Collaborative fraud detection across different financial institutions without sharing raw dataBuilding recommendation systems using user interaction graphs distributed across multiple platformsAnalyzing distributed social networks while preserving user privacy

Competitive Edge

Offers a novel solution for subgraph federated learning that explicitly addresses privacy and scalability, differentiating it from existing approaches.

Market Opportunity

Growing market for privacy-preserving AI and federated learning solutions.

Revenue Models

Licensing of the FedLap frameworkoffering secure federated learning services.

Resource Requirements

Compute Needs

Requires distributed compute resources for federated learning training.

Data Requirements

Requires graph-structured data distributed across multiple clients.

Deployment Constraints

Complexity of distributed systems, communication overhead, ensuring robust privacy guarantees.

Scalability

Explicitly designed for scalability in subgraph federated learning scenarios.

Regulatory Considerations

GDPRCCPAData privacy regulations

Production Readiness

Maturity Level

Research

Time to Market

2-4 years for practical deployment in sensitive industries.

Patent Potential

High, for the FedLap framework and its privacy-preserving mechanisms.

View Full Paper Back to Papers