Ephemeral ML Model Training via Gradient Processing
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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 privacy and security benefits.
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, ensuring data security and privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If federated learning is used to train ML models locally on client devices, then data privacy and security are improved, but the ability to leverage remote system processing capabilities is lost
Solution Approach 1:
The patent introduces an intermediary approach where the remote system processes encrypted or differential privacy-protected versions of client data to generate training gradients, which are then sent back to client devices. This intermediary processing layer allows the remote system to contribute its computational capabilities while maintaining data privacy through cryptographic or statistical protection mechanisms.
2Adaptability or versatility
If client data is transmitted to and processed by a remote system, then remote system processing capabilities can be leveraged, but data privacy and security are compromised
Solution Approach 1:
The patent employs disposable cryptographic protections such as one-time pads, ephemeral encryption keys, or single-use differential privacy mechanisms that are discarded after a single use. These short-lived protective layers enable secure remote processing without requiring long-term storage or management of sensitive cryptographic materials.
Solution Approach 2:
The patent transforms client data into different parameter representations before transmission to the remote system, such as encrypted vectors, differential privacy noise-added data, or transformed feature spaces. These parameter changes maintain the statistical properties needed for training while removing directly identifiable information.
3Object-affected harmful factors
If traditional federated learning is used with local on-device processing, then data remains local and private, but model precision and recall may be limited by device capabilities
Solution Approach 1:
The patent segments the model training process into distinct phases: local data preprocessing and feature extraction on client devices, encrypted gradient computation at the remote system, and model aggregation back at client devices. This segmentation allows each component to operate in its optimal environment while maintaining overall privacy guarantees.
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.