Federated Learning Privacy Scheme Using Homomorphic Encryption
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Solution Overview
Problem
Conventional federated learning environments provide insufficient data privacy guarantees, leading to potential inferences from individual participants and risks of collusion among parties, while attempts to ensure privacy often result in poor predictive model performance due to excessive noise injection.
Innovation Solution
A system employing a data privacy scheme that combines privacy processes such as anonymization, randomization, and differential privacy with homomorphic encryption, specifically threshold variant homomorphic encryption, to synthesize machine learning models in a federated learning environment, ensuring strong privacy guarantees and minimizing noise in the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If privacy processes are applied in federated learning to ensure data privacy guarantees, then privacy protection is improved, but model performance deteriorates due to excessive noise injection
Solution Approach 1:
The patent applies differential privacy by adding calibrated noise to model parameters during the federated learning process. The noise scale is carefully controlled based on privacy budget parameters (epsilon, delta) to achieve strong privacy guarantees while minimizing the impact on model accuracy. This parameter-based approach allows systematic trade-off optimization between privacy and utility.
Solution Approach 2:
The patent combines multiple privacy preservation techniques including differential privacy, homomorphic encryption, and secure multi-party computation into a composite privacy framework. This layered approach provides both strong privacy guarantees and maintains model performance by using encryption for sensitive operations and differential privacy for parameter updates, avoiding excessive noise injection.
2Reliability
If homomorphic encryption is used to protect data in federated learning, then data security is improved, but computational complexity increases
Solution Approach 1:
The patent segments the federated learning process into distinct phases: encryption-based secure communication for model parameter exchange, and differential privacy-based noise addition for privacy protection. Homomorphic encryption is applied only where necessary for secure aggregation, reducing overall computational overhead compared to applying it throughout the entire training process.
Solution Approach 2:
The patent introduces a trusted aggregator server that performs homomorphic encryption operations to aggregate model parameters from multiple clients. This intermediary handles the computationally intensive encryption/decryption operations centrally, reducing the burden on individual client devices and optimizing the overall system's computational efficiency.
3Reliability
If multiple privacy processes are combined to strengthen privacy guarantees, then privacy protection is improved, but system complexity increases
Solution Approach 1:
The patent merges differential privacy and homomorphic encryption into a unified federated learning framework where they complement each other. Differential privacy handles the privacy preservation during model updates, while homomorphic encryption secures the communication channel. This integration creates a cohesive system that achieves strong privacy guarantees without requiring separate independent systems.
Solution Approach 2:
The patent performs preliminary setup of encryption keys and privacy parameters before the federated learning process begins. Homomorphic encryption keys are generated in advance, and differential privacy budgets are allocated beforehand. This preliminary configuration simplifies the runtime execution by avoiding complex dynamic decisions during training.
Data Source
AI summary
Techniques regarding privacy preservation in a federated learning environment are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a plurality of machine learning components that can execute a machine learning algorithm to generate a plurality of model parameters. The computer executable components can also comprise an aggregator component that can synthesize a machine learning model based on an aggregate of the plurality of model parameters. The aggregator component can communicate with the plurality of machine learning components via a data privacy scheme that comprises a privacy process and a homomorphic encryption process in a federated learning environment.