Privacy Preserving Peer-to-Peer Machine Learning via Homomorphic Encryption
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Solution Overview
Problem
Current machine learning techniques face challenges in preserving user data privacy while maintaining accurate models, particularly in decentralized learning paradigms like Federated Learning, which are vulnerable to information leakage attacks and require complex infrastructure setups.
Innovation Solution
A peer-to-peer machine learning method utilizing partial homomorphic encryption for federated averaging, allowing users to train a global model locally without sharing raw data, thus preventing information leakage and eliminating the need for centralized infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning is used to enable local training on user devices, then data privacy is improved, but model parameters can still leak sensitive information and require centralized infrastructure
Solution Approach 1:
The patent segments the centralized federation server functionality into distributed peer-to-peer interactions. Each user device independently performs federated averaging with other devices without requiring a central coordinator, eliminating the single point of failure and centralized bottleneck while maintaining privacy guarantees.
Solution Approach 2:
The patent introduces partial homomorphic encryption as an intermediary mechanism that enables secure computation between peers. This cryptographic primitive allows devices to compute federated averages of model parameters without revealing the actual parameters to each other, providing privacy preservation without requiring trusted centralized infrastructure.
2Reliability
If differential privacy is applied to protect shared model parameters, then information leakage attacks are prevented, but utility and performance are lost
Solution Approach 1:
The patent changes the cryptographic parameter from full differential privacy (which adds noise) to partial homomorphic encryption (which preserves exact values). This parameter change in the privacy mechanism allows protection against information leakage attacks while maintaining the exact utility of model parameters for training, avoiding the accuracy degradation inherent in differential privacy approaches.
3Ease of operation
If centralized servers are used to orchestrate federated learning, then training coordination is simplified, but scalability is limited and single point of failure exists
Solution Approach 1:
The patent segments the centralized coordination function into distributed peer-to-peer interactions. Each device independently selects peers and performs federated averaging without central orchestration, enabling the system to scale to large numbers of participants without creating a bottleneck at a central server.
4Reliability
If full homomorphic encryption is used for private aggregation, then data privacy is maximized, but computational overhead and complexity increase significantly
Solution Approach 1:
The patent applies partial homomorphic encryption rather than full homomorphic encryption. This partial application provides sufficient privacy protection for the federated averaging operation while significantly reducing the computational overhead and implementation complexity compared to full homomorphic encryption schemes.
Data Source
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AI summary
A method and system for providing privacy preserving peer-to-peer ML for users to participate in a collaborative learning scheme. The provided peer-to-peer learning solution does not require any sort of centralized infrastructure and enables users to benefit from the wealth of user data that are distributed across the participating devices while maintaining accurate personalized models. In the proposed approach, users (through their electronic user devices) train an improved global ML model without the need for federation; instead, they train models locally and organize small synergies, where they perform federated averaging on their model's parameters via a homomorphic based protocol.