FedSpectral+: Spectral Clustering using Federated Learning

TL;DR

This paper introduces FedSpectral+ and FedSpectral, federated learning-based spectral clustering algorithms that address privacy and scalability issues in graph clustering, achieving high similarity to centralized methods.

Contribution

The paper proposes novel federated spectral clustering algorithms, FedSpectral and FedSpectral+, that preserve privacy and improve scalability while maintaining clustering quality.

Findings

FedSpectral+ achieves over 98.8% similarity to centralized clustering.

The methods effectively balance privacy, scalability, and clustering accuracy.

FedSpectral+ outperforms baseline approaches in distributed graph clustering.

Abstract

Clustering in graphs has been a well-known research problem, particularly because most Internet and social network data is in the form of graphs. Organizations widely use spectral clustering algorithms to find clustering in graph datasets. However, applying spectral clustering to a large dataset is challenging due to computational overhead. While the distributed spectral clustering algorithm exists, they face the problem of data privacy and increased communication costs between the clients. Thus, in this paper, we propose a spectral clustering algorithm using federated learning (FL) to overcome these issues. FL is a privacy-protecting algorithm that accumulates model parameters from each local learner rather than collecting users' raw data, thus providing both scalability and data privacy. We developed two approaches: FedSpectral and FedSpectral+. FedSpectral is a baseline approach that uses local spectral clustering labels to aggregate the global spectral clustering by creating a similarity graph. FedSpectral+, a state-of-the-art approach, uses the power iteration method to learn the global spectral embedding by incorporating the entire graph data without access to the raw information distributed among the clients. We further designed our own similarity metric to check the clustering quality of the distributed approach to that of the original/non-FL clustering. The proposed approach FedSpectral+ obtained a similarity of 98.85% and 99.8%, comparable to that of global clustering on the ego-Facebook and email-Eu-core dataset.