Distributable Data Models for Privacy-Preserving Transfer Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional machine learning approaches face challenges in transferring and adapting data models trained in specific environments due to differences in feature spaces and distributions, especially when data is sensitive and cannot be easily centralized, requiring a system to accommodate diverse data models without identical feature spaces or distributions.
Innovation Solution
A system and method for using transfer learning with distributable models, where a network-connected model source serves instances of generalized and bias-specific models, allowing devices to train them locally, generate update reports, and improve the main models, while maintaining data privacy and adhering to regulatory constraints by cleansing and sanitizing data within the devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is centralized for model training, then model training efficiency is improved, but data privacy and security are compromised
Solution Approach 1:
The patent introduces federated learning as an intermediary approach where a central server coordinates model training without directly accessing sensitive data. The server distributes model parameters to edge devices, which train locally using their own data and send back only model updates. This mediator architecture enables collaborative model improvement while preserving data privacy and security at the source.
Solution Approach 2:
The patent segments the centralized training process into distributed local training tasks across multiple edge devices. Each device independently trains model parameters on its local data subset, then contributes updates to the global model. This segmentation allows parallel processing and improved efficiency while keeping sensitive data localized and secure.
2Object-affected harmful factors
If data is distributed across multiple devices, then data privacy is maintained, but model training and adaptation become more complex
Solution Approach 1:
The patent implements a universal federated learning framework that handles multiple device types, data formats, and training scenarios through a standardized architecture. The central server provides multi-functional capabilities including model distribution, aggregation, coordination, and management across heterogeneous edge devices. This universal approach simplifies the complexity by providing a unified interface and process that works across diverse distributed environments.
Solution Approach 2:
The patent establishes a feedback loop where edge devices send model parameter updates back to the central server, which aggregates these updates and redistributes the improved global model. This iterative feedback mechanism automatically handles the complexity of distributed training through systematic coordination, eliminating the need for complex manual management of distributed training processes.
3Adaptability or versatility
If transfer learning is applied across different domains, then model adaptability is improved, but feature space mismatches reduce training effectiveness
Solution Approach 1:
The patent applies domain adaptation techniques that transform and align feature spaces across different domains by adjusting model parameters. The federated learning process automatically adapts model parameters to local domain characteristics while maintaining compatibility with the global model. This parameter transformation enables effective transfer learning across domains with different feature distributions without requiring manual feature alignment.
Data Source
AI summary
A system for transfer learning and domain adaptation using distributable data models is provided, comprising a network-connected distributable model configured to serve instances of a plurality of distributable models; and a directed computation graph module configured to receive at least an instance of at least one of the distributable models from the network-connected computing system, create a second dataset from machine learning performed by a transfer engine, train the instance of the distributable model with the second dataset, and generate an update report based at least in part by updates to the instance of the distributable model.