Neural Graph Revealer Aggregation for Scalable Federated Learning
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Solution Overview
Problem
Existing federated learning frameworks face challenges in managing model parameter explosion and data privacy as the number of clients increases, particularly when deploying large-scale computing infrastructure for deep neural networks.
Innovation Solution
A federated learning platform utilizing Neural Graph Revealers (NGRs) aggregates locally trained models from clients without sharing private data, generating a global NGR model that learns averaged information while maintaining privacy, and employs a stitching procedure to personalize client-specific models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If federated learning aggregates models from many clients, then model accuracy and predictive power increase, but model parameter size explodes
Solution Approach 1:
The patent extracts only the essential structural information from client models by converting them to NGR representations. Instead of aggregating all model parameters, the system identifies and retains only the critical graph structure elements that capture the most important relationships, discarding redundant parameters while preserving predictive accuracy.
Solution Approach 2:
The patent inverts the traditional approach by first converting models to NGR structural representations before aggregation. This inversion allows the system to work with compact graph structures rather than large parameter sets, fundamentally changing the aggregation process to avoid parameter explosion while maintaining model quality.
2Measurement precision
If private datasets are shared across clients, then global model training improves, but data privacy is compromised
Solution Approach 1:
The NGR structure serves as an intermediary representation that enables knowledge sharing without direct data sharing. Clients convert their private data into NGR structural representations, which are then aggregated globally. This intermediary form preserves the essential patterns needed for training while preventing access to sensitive raw data.
Solution Approach 2:
The patent creates and shares copies of structural information (NGR representations) rather than original data. These NGR copies capture the essential relationships and patterns from private datasets, allowing global learning without exposing the actual sensitive data to other clients or the central server.
3Adaptability or versatility
If computing infrastructure scales to handle more clients, then personalized model generation improves, but computational cost and power consumption increase
Solution Approach 1:
The patent changes the fundamental parameters of model representation from dense neural network weights to sparse NGR graph structures. This parameter transformation reduces the computational complexity of model operations, enabling personalized model generation for more clients with lower energy consumption while maintaining adaptability.
Data Source
AI summary
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.