Machine learning prediction of network dynamics with privacy protection

TL;DR

This paper introduces a federated graph neural network framework that enables privacy-preserving prediction of network dynamics across distributed data sources, validated through simulations and real-world influenza spread prediction.

Contribution

The paper presents a novel federated GNN approach for predicting network dynamics without sharing sensitive data, addressing privacy and data availability challenges.

Findings

Successful prediction of diverse network dynamics in simulations.

Effective real-world influenza spread prediction across US states.

Framework preserves privacy while enabling collaborative modeling.

Abstract

Predicting network dynamics based on data, a problem with broad applications, has been studied extensively in the past, but most existing approaches assume that the complete set of historical data from the whole network is available. This requirement presents a great challenge in applications, especially for large, distributed networks in the real world, where data collection is accomplished by many clients in a parallel fashion. Often, each client only has the time series data from a partial set of nodes and the client has access to only partial timestamps of the whole time series data and partial structure of the network. Due to privacy concerns or license related issues, the data collected by different clients cannot be shared. To accurately predict the network dynamics while protecting the privacy of different parties is a critical problem in the modern time. Here, we propose a solution based on federated graph neural networks (FGNNs) that enables the training of a global dynamic model for all parties without data sharing. We validate the working of our FGNN framework through two types of simulations to predict a variety of network dynamics (four discrete and three continuous dynamics). As a significant real-world application, we demonstrate successful prediction of State-wise influenza spreading in the USA. Our FGNN scheme represents a general framework to predict diverse network dynamics through collaborative fusing of the data from different parties without disclosing their privacy.