Federated Learning Noise Communication for Privacy-Aware Model Training
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Solution Overview
Problem
Federated learning models often produce insufficiently accurate outputs for real-world prediction tasks due to insufficient training data and real-world biases.
Innovation Solution
Client computing devices generate noise data using labeled images and update a distributed instance of a machine learning model, which is then transmitted to a server for further training and parameter updates, allowing for improved model accuracy through expanded training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If federated learning uses only device-specific training data, then model adaptability to specific devices improves, but model accuracy for real-world predictions deteriorates
Solution Approach 1:
The patent combines device-specific training data with synthesized noise data to create a hybrid training dataset. This merging approach allows the model to maintain adaptability to specific devices while incorporating diverse patterns from noise data, thereby improving real-world prediction accuracy without sacrificing device-specific performance
Solution Approach 2:
The system performs preliminary data synthesis by generating noise data with realistic statistical properties before model training. This preliminary action creates a enriched training dataset that anticipates real-world variations, allowing the model to be pre-adapted to diverse conditions before deployment, thus improving prediction accuracy while maintaining device adaptability
2Quantity of substance
If federated learning aggregates data from multiple devices, then training data quantity increases, but data privacy and security risks worsen
Solution Approach 1:
The patent introduces synthesized noise data as an intermediary that bridges the gap between privacy protection and training data quantity. Instead of directly aggregating sensitive device data, the system uses noise data with realistic statistical properties as a mediator, allowing the model to learn from diverse patterns without exposing actual user data, thus increasing training data quantity while maintaining privacy security
Solution Approach 2:
The system creates synthetic copies of training data through noise generation with matched statistical properties. These copied data samples replicate the essential characteristics and variations of real device data without containing actual sensitive information, enabling the model to learn from expanded training data while preserving data privacy and security
Data Source
AI summary
In some embodiments, a method includes sending, from a first set of computing devices, a distributed instance of a machine learning model to a client computing device, where the client computing device is caused to provide a set of outputs related to the noise data, and where the set of outputs is an output of the distributed instance derived from inputting the noise data into the distributed instance. The method further includes receiving the set of outputs from the client computing device and configuring another instance of the machine learning model based on the noise data and the set of outputs related to the noise data.