Distributed Self-Supervised Learning With Embedding Statistics Privacy
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Solution Overview
Problem
Training machine learning models using user images while preserving user privacy is challenging, as centralizing these images compromises privacy and existing self-supervised methods like Barlow Twins still require access to individual images.
Innovation Solution
A distributed training approach where user devices compute local embedding statistics and send only aggregated data to a server, which determines global statistics and updates, ensuring images remain on user devices, thus protecting privacy while effectively training image representation models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If training images are centralized on a server for model training, then model training effectiveness is improved, but user privacy is compromised
Solution Approach 1:
The training process is segmented into local execution on user devices and centralized aggregation on server. Each user device independently computes embedding statistics from its local images without transmitting the images themselves, thereby segmenting the training workload and data handling between distributed nodes and central server.
Solution Approach 2:
The essential training information (embedding statistics) is extracted from the raw training images and transmitted to the server, while the sensitive raw images remain localized on user devices. This extraction approach transmits only the necessary statistical data for training without exposing private image content.
2Measurement precision
If self-supervised learning methods like Barlow Twins are used, then model accuracy with limited labeled data is improved, but access to individual images is still required
Solution Approach 1:
Embedding statistics serve as an intermediary representation that captures the essential information needed for self-supervised learning without requiring direct access to individual images. The server operates on these statistical summaries rather than raw images, enabling Barlow Twins-style training with privacy preservation.
3Productivity
If distributed training across user devices is implemented, then resource efficiency is improved, but system complexity increases
Solution Approach 1:
User devices autonomously perform the computationally intensive embedding computation and statistical aggregation locally, serving themselves as training nodes without requiring complex centralized coordination. This self-service approach leverages idle computing resources across the distributed network while minimizing server-side complexity.
Data Source
AI summary
Methods, systems, and apparatus, including medium-encoded computer program products, for receiving, from a first set of user devices, embedding statistics that were determined by the user devices using sets of one or more training pairs. Global embedding statistics can be determined, at least in part, using the embedding statistics, and transmitted to a second set of user devices. Local parameter model updates that were determined, at least in part, using the global embedding statistics can be received from the second set of user devices. Global model updates can be determined at least in part and using at least a subset of the local model updates. Global model updates can be transmitted to a third set of user devices.