FEDERATED LEARNING WITH NEURAL GRAPH REVEALERS

Abstract

Methods and apparatuses are described for providing a federated learning platform that utilizes Neural Graph Revealers, which are a type of Probabilistic Graphical Model (PGM). The federated learning platform generates and stores Neural Graph Revealers using sparse graph recovery techniques by aggregating client models that were trained using private datasets. Each client may generate a locally trained NGR model that is trained using data that is private to that client, and then the locally trained NGR models for each client may be aggregated to generate a global NGR model. The federated learning platform may maintain a global NGR model that learns the averaged information from the local trained NGR models associated with each client while the training data for each client is kept secure within the client's environment.

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