Federated Learning for Edge AI Model Privacy
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Solution Overview
Problem
Current distributed machine learning models face challenges in learning universal vector representations across edge devices without sharing user data, due to non-uniformity of data and difficulty in handling cross-domain data, while existing solutions either breach data privacy or are limited by small data sets.
Innovation Solution
A system and method for distributed learning of universal vector representations on edge devices, where each device trains an on-device AI model locally and transmits it to a server for aggregation, generating a global AI model that can learn universal representations across devices without exposing user data, enabling generic cross-domain data utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If centralized ML models are used to collect data from edge devices for training, then the model can learn from diverse data, but data privacy is breached and users are unwilling to share private data
Solution Approach 1:
The patent introduces federated learning as an intermediary approach between centralized and fully distributed training. The central server coordinates the training process by distributing model parameters to edge devices, collecting trained models back, and aggregating them. This mediator role enables diverse data utilization while maintaining data locality and privacy, as the actual training data never leaves the edge devices.
Solution Approach 2:
The patent implements local model training on edge devices using locally available data. Each edge device trains its own ML model using its private data, ensuring data remains local and privacy is maintained. The local training process adapts the global model to local data characteristics while contributing to the overall system knowledge through periodic aggregation at the central server.
2Object-affected harmful factors
If on-device ML models are trained using individual data, then data privacy is maintained, but the models are limited to simple problems and cannot share knowledge across users
Solution Approach 1:
The patent combines multiple individually trained models from different edge devices into a single aggregated global model at the central server. This merging process integrates knowledge from diverse users and devices while maintaining the privacy benefits of local training. The aggregation step enables cross-user knowledge sharing by combining the learned patterns from multiple local models into a comprehensive global model that benefits all participants.
3Object-affected harmful factors
If distributed ML models are trained on edge devices without data sharing, then data privacy is maintained, but non-uniformity of user data and difficulty in learning from cross-domain data are challenges
Solution Approach 1:
The patent implements a dynamic federated learning process where the global model parameters are continuously updated and redistributed to edge devices based on performance feedback. The system dynamically adapts to the non-uniformity of local data distributions by allowing each device to train on its specific data characteristics while the global model learns to generalize across domains. This dynamic iteration process enables the system to handle cross-domain data effectively despite initial non-uniformity.
Data Source
AI summary
A method and system for learning universal vector representation of concepts in a distributed environment comprising a plurality of edge devices are provided. The method includes obtaining data from one or more sources available at the candidate edge device, determining a plurality of concepts from the obtained data, training on-device artificial intelligence (AI) model locally available at the candidate edge device using the plurality of concepts. The method also includes transmitting the at least one trained on-device AI model to a server and receiving a global AI model for deployment from the server. The method further includes deploying the global AI model for universal vector representation of concepts in the candidate edge device.