Federated Learning Privacy via Noisy Local Parameter Updates
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Solution Overview
Problem
Federated learning systems face challenges in ensuring user-level privacy, as existing methods like Differential Privacy (DP) may expose a user's data distribution to the federation server, compromising privacy even when individual data items are protected.
Innovation Solution
Implementing user-level privacy preservation in federated machine learning by applying noise to parameter updates locally at the user level before sending them to the aggregation server, ensuring that the user's data distribution remains obscured from the federation server.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Differential Privacy (DP) is applied to protect individual data items, then individual data privacy is improved, but user data distribution is exposed to the federation server
Solution Approach 1:
The patent segments the privacy protection mechanism into two distinct layers: (1) item-level DP that protects individual data points within each user's dataset, and (2) user-level DP that protects the overall data distribution characteristics. This segmentation allows each layer to address specific privacy concerns independently, preventing the exposure of data distribution while maintaining individual item privacy.
Solution Approach 2:
The patent applies different privacy protection qualities at different levels: local differential privacy (item-level) is applied to individual data items, while global differential privacy (user-level) is applied to the aggregate parameter updates. This local quality approach ensures that each layer provides the appropriate level of protection needed for its specific function.
2Reliability
If user-level privacy preservation is implemented by applying noise to parameter updates, then user data distribution privacy is improved, but model training accuracy may deteriorate
Solution Approach 1:
The patent applies partial noise to parameter updates rather than complete obfuscation. By carefully calibrating the noise level to provide just enough protection for data distribution privacy while maintaining sufficient signal for accurate model training, the system achieves the optimal balance between privacy and utility without completely sacrificing model performance.
Solution Approach 2:
The patent dynamically adjusts the noise parameter (privacy budget) to optimize the trade-off between privacy protection and model accuracy. By changing the noise level based on the specific training context, data sensitivity, and privacy requirements, the system can adaptively balance the contradiction between protecting data distribution and maintaining training precision.
Data Source
AI summary
User-level privacy preservation is implemented within federated machine learning. An aggregation server may distribute a machine learning model to multiple users each including respective private datasets. Individual users may train the model using the local, private dataset to generate one or more parameter updates. Prior to sending the generated parameter updates to the aggregation server for incorporation into the machine learning model, a user may modify the parameter updates by applying respective noise values to individual ones of the parameter updates to ensure differential privacy for the dataset private to the user. The aggregation server may then receive the respective modified parameter updates from the multiple users and aggregate the updates into a single set of parameter updates to update the machine learning model. The federated machine learning may further include iteratively performing said sending, training, modifying, receiving, aggregating and updating steps.