Custom Machine Learning Algorithms With Privacy-Preserving Edge Training
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Solution Overview
Problem
Existing machine learning systems face challenges in handling continuous data updates and privacy concerns, leading to excessive cloud traffic and computational inefficiencies, especially in applications like online recommendation systems and edge computing environments.
Innovation Solution
A distributed AI system employing asynchronous federated learning, where edge devices perform localized training using lazy learning techniques, sharing hyperparameters with the cloud to create customized models, reducing cloud usage and bandwidth requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If centralized cloud-based machine learning training is used to handle continuous data updates, then model accuracy can be maintained, but cloud bandwidth consumption and computational load increase excessively
Solution Approach 1:
The system segments the centralized training process into distributed local training sessions across multiple edge devices. Each device independently trains models locally using its own data, eliminating the need to continuously upload data to the cloud. The segmented approach maintains model accuracy through local computation while dramatically reducing cloud bandwidth consumption.
Solution Approach 2:
The system transitions from a single centralized training dimension to multiple parallel distributed training dimensions across edge devices. By adding the spatial dimension of distributed computation, the system achieves the same model accuracy improvement while reducing the bandwidth dimension through local processing.
2Adaptability or versatility
If user data is collected and processed centrally in the cloud for continuous model updates, then personalized recommendations can be improved, but user privacy concerns increase
Solution Approach 1:
The system extracts the training computation from the centralized cloud environment and relocates it to distributed edge devices. By taking out the data processing function from the cloud, user data remains local on user devices, eliminating privacy concerns while maintaining personalization capability through local model training.
Solution Approach 2:
Each edge device performs self-service local training using its own user data without requiring data to leave the device. The device independently updates its model parameters based on local data patterns, achieving personalized recommendations while keeping data private and local.
3Measurement precision
If frequent model updates are performed using eager learning to generalize training data, then query response accuracy improves, but computational overhead and training time increase
Solution Approach 1:
The system implements periodic lazy learning updates instead of continuous eager learning. Models are updated periodically based on accumulated local data patterns rather than continuously generalizing from all training data. This periodic approach maintains query response accuracy while significantly reducing computational overhead and training time.
Solution Approach 2:
The system dynamically switches between lazy learning (delayed generalization) and periodic model updates based on data accumulation and performance thresholds. This dynamic approach optimizes the balance between query accuracy and computational efficiency by performing generalization only when necessary.
Data Source
AI summary
An embodiment of the invention may include a method, computer program product, and computer system for managing a machine learning algorithm. The embodiment may include a computing device that distributes a first algorithm to a plurality of computing devices. The embodiment may include updates to the first algorithm, to create a second algorithm, by a first device of the plurality of computing devices. The first device is grouped with other devices in a first cluster of devices of the plurality of computing devices. The first cluster of computing devices comprises more than one computing device. Updating the first algorithm is performed based on information shared amongst the first cluster of devices.