FedSpectral+: Spectral Clustering using Federated Learning
- URL: http://arxiv.org/abs/2302.02137v1
- Date: Sat, 4 Feb 2023 09:28:36 GMT
- Title: FedSpectral+: Spectral Clustering using Federated Learning
- Authors: Janvi Thakkar, Devvrat Joshi
- Abstract summary: We propose a spectral clustering algorithm using federated learning (FL) to overcome issues of data privacy and increased communication costs.
FL is a privacy-protecting algorithm that accumulates model parameters from each local learner rather than collecting users' raw data.
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 method to learn the global spectral embedding by incorporating the entire graph data without access to the raw information distributed among the clients.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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