Dynamic Differential Privacy in Federated Learning
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Solution Overview
Problem
Federated machine learning faces challenges in sharing data across stakeholders due to data privacy regulations, requiring a method to balance data transparency and privacy protection while ensuring sensitive information remains secure.
Innovation Solution
Implementing dynamic differential privacy by introducing noise, anonymization, and encryption in the federated learning process, with distinct hierarchies of loss requirements applied to input and model data, allowing stakeholders to share analytics without exposing private information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is shared across stakeholders for federated learning, then the quality of machine learning models is improved through aggregated data analysis, but data privacy and security are compromised due to exposure of sensitive information
Solution Approach 1:
The patent introduces a coordinator as an intermediary component that manages the federated learning process. The coordinator aggregates model data from multiple data owners and applies differential privacy mechanisms, acting as a mediator that enables collaboration while protecting individual data owners' privacy. This intermediary structure allows the system to achieve both model quality improvement through aggregation and privacy protection through centralized privacy management.
Solution Approach 2:
The patent applies differential privacy by introducing controlled noise parameters to the model data during aggregation. By adjusting the privacy budget parameter (epsilon) and noise scale, the system dynamically balances between maintaining model accuracy and protecting data privacy. This parameter-based approach allows flexible trade-off management between the conflicting goals of model quality and privacy preservation.
2Object-affected harmful factors
If differential privacy is applied to protect data privacy, then data security is improved, but the quality of aggregated model data deteriorates due to noise introduction
Solution Approach 1:
The patent applies differential privacy selectively to specific components of the federated learning process rather than uniformly to all data. By applying privacy mechanisms only to the aggregated model data updates rather than to raw input data or model architecture, the system achieves necessary privacy protection while minimizing the impact on model quality. This partial application reduces the cumulative noise effect while maintaining privacy guarantees.
Solution Approach 2:
The patent implements dynamic differential privacy where the privacy budget and noise parameters are adjusted adaptively based on the stage of federated learning and the sensitivity of the data. The system dynamically modifies the level of privacy protection applied at different aggregation rounds, allowing higher model quality when privacy risk is lower and stronger privacy protection when needed, thus balancing the trade-off between privacy and model quality throughout the learning process.
3Object-affected harmful factors
If multiple hierarchies of loss requirements are applied to different data stages, then data privacy is maintained across different data forms, but the system complexity increases
Solution Approach 1:
The patent divides the federated learning data flow into distinct segments or stages (input data stage, model training stage, aggregation stage), and applies appropriate privacy mechanisms to each segment. By segmenting the privacy protection approach according to the data stage and sensitivity, the system maintains comprehensive privacy coverage while avoiding the need for a single complex unified mechanism. Each segment can use optimized privacy techniques suited to its specific requirements.
Data Source
AI summary
Embodiments of the present disclosure provide hierarchical, differential privacy enhancements to federated, machine learning. Local machine learning models may be generated and/or trained by data owners participating in the federated learning framework based on their respective data sets. Noise corresponding to and satisfying a first privacy loss requirement are introduced to the data owners' respective data sets, and noise corresponding to and satisfying a first privacy loss requirement are introduced to the local models generated and/or trained by the data owners. The data owners transmit model data corresponding to their respective local models to a coordinator, which in turn aggregates the data owners' model data. After introducing noise corresponding to and satisfying a third privacy loss requirement to the aggregated model data, the coordinator transmits the aggregated model data to the data owners to facilitate updating and/or re-training on their respective machine learning models.