Ephemeral ML Model Training via Gradient Extraction
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Solution Overview
Problem
Federated learning of machine learning models is not well-suited for scenarios where client data is processed by a remote system, leading to privacy and security concerns, as data is transmitted and processed remotely, undermining local data considerations.
Innovation Solution
Ephemeral learning, where a remote system processes client data streams to generate gradients for updating global machine learning models using unsupervised learning, without storing or logging the data, and transmits updated models back to client devices, maintaining data security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning is used to train ML models locally on client devices, then data privacy and security are improved, but the system cannot leverage remote system processing capabilities and is unsuitable for scenarios where data must be transmitted to the remote system
Solution Approach 1:
The system segments the learning process into two distinct pipelines: a fulfillment pipeline that processes client data through remote systems for service fulfillment, and a training pipeline that processes client data for generating training gradients. This segmentation allows each pipeline to serve its specific purpose while maintaining data privacy - the fulfillment pipeline uses remote processing when needed, while the training pipeline extracts learning signals without requiring data storage.
Solution Approach 2:
The system extracts only the essential training information (gradients) from client data processing, separating the learning signal from the actual client data. By extracting gradients from both fulfillment and training pipelines and using them to update global model weights, the system obtains training benefits without retaining client data, thus maintaining privacy while leveraging remote processing capabilities.
2Productivity
If client data is transmitted to and processed by a remote system, then the system can leverage remote processing capabilities, but data privacy and security are compromised as data leaves the client device
Solution Approach 1:
The system applies different processing qualities to different data streams: client data processed by the fulfillment pipeline maintains its original privacy requirements (not stored), while client data processed by the training pipeline is specifically transformed into gradients for model updating. Each pipeline processes data with appropriate privacy safeguards - the fulfillment pipeline for service delivery and the training pipeline for model improvement without storage.
Solution Approach 2:
The system recovers training value from client data by extracting gradients in the training pipeline, then discards the original client data after gradient extraction. The gradients are used to update global model weights, and the original client data is not stored or retained, thus recovering the learning signal while discarding the privacy-sensitive data.
3Manufacturing precision
If client data is stored or logged in remote system memory for processing, then the system can perform comprehensive training, but data security and privacy benefits are undermined
Solution Approach 1:
The system uses ephemeral data processing where client data exists only temporarily in memory during pipeline execution and is then discarded. The training pipeline processes client data to generate gradients without persistent storage, using cheap, short-lived data representations that are immediately discarded after extracting training value, thus achieving training accuracy without compromising security through data retention.
4Manufacturing precision
If the remote system processes and stores client data for model training, then comprehensive model updates can be achieved, but the complexity of data management and security protocols increases
Solution Approach 1:
The system extracts only the necessary training gradients from client data processing, removing the need to manage and secure large volumes of raw client data. By extracting gradients in both pipelines and using them for model updates, the system simplifies data management - gradients are mathematical representations that can be securely transmitted and stored without the complexity of managing sensitive client data.
Data Source
AI summary
Implementations disclosed herein are directed to ephemeral learning of machine learning (“ML”) model(s) based on gradient(s) generated at a remote system (e.g., remote server(s)). Processor(s) of the remote system can receive stream(s) of audio data capturing spoken utterance(s) from a client device of a user. A fulfillment pipeline can process the stream(s) of audio data to cause certain fulfillment(s) of the spoken utterance(s) to be performed. Meanwhile, a training pipeline can process the stream(s) of audio data to generate gradient(s) using unsupervised learning techniques. Subsequent to the processing by the fulfillment pipeline and/or the training pipeline, the stream(s) of audio data are discarded by the remote system. Accordingly, the ML model(s) can be trained at the remote system without storing or logging of the stream(s) of audio data by non-transient memory thereof, thereby providing more efficient training mechanisms for training the ML model(s) and also increasing security of user data.