Distributed Machine Learning for Privacy Preservation
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Solution Overview
Problem
Existing machine learning technologies face challenges in preserving user privacy during model training, as they often require transmitting private client data to centralized servers, which can compromise data security and privacy.
Innovation Solution
Implementing a distributed machine learning approach where client devices train models locally using private data and transmit updated model data to servers, incorporating randomness to obscure original data and maintaining a pool of model data to avoid tracking specific client data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If centralized server training is used, then model training efficiency is improved, but user privacy is compromised
Solution Approach 1:
The training process is segmented into two parts: (1) centralized server distributes model parameters to multiple client devices, and (2) each client device independently trains the model locally using its own private data. This segmentation allows the system to maintain centralized coordination while preserving user privacy by keeping sensitive data on local devices.
Solution Approach 2:
The patent introduces an intermediary mechanism where clients train models locally and only share updated model parameters (not raw data) with the server. The server acts as an intermediary that aggregates these updates without ever seeing the original private data, thus mediating between centralized training efficiency and privacy preservation.
2Measurement precision
If client data is transmitted to servers, then model training accuracy is improved, but data security is compromised
Solution Approach 1:
The patent extracts only the essential training information (model parameters and gradients) from the client devices and transmits only these extracted elements to the server, leaving the sensitive raw data on local devices. This extraction approach maintains training accuracy while eliminating the security risk of transmitting complete datasets.
Solution Approach 2:
Instead of transmitting original private data, the system creates and transmits copies in the form of model parameters and updates. These copies contain the learned patterns and insights necessary for accurate training while being mathematically transformed to exclude any directly identifiable personal information.
3Object-affected harmful factors
If distributed training is implemented, then user privacy is protected, but system complexity increases
Solution Approach 1:
The patent implements a universal protocol that can be applied across diverse client devices and training scenarios. The same distributed training framework, parameter exchange format, and aggregation mechanism work consistently whether there are 10 or 10,000 clients, reducing the need for device-specific customizations and managing system complexity through standardization.
Data Source
AI summary
Methods, computer readable media, devices, and systems provide for distributed machine learning. In one aspect, a method of training a model is disclosed. The method includes receiving, by a client device, from one or more servers, an intermediate model, training, by the client device, the intermediate model based on private data, and transmitting, by the client device, to the one or more servers, the trained intermediate model.