Consensus Pattern Dictionary for Private Distributed IoT Learning
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Solution Overview
Problem
Existing decentralized learning frameworks face challenges in securely sharing and aggregating low-level IoT data for collaborative learning without incurring significant computational overhead, as traditional cryptographic methods are inadequate for protecting real-valued data and security requirements differ from financial transactions.
Innovation Solution
A collaborative learning framework that utilizes a decentralized network of client systems to build a consensus-based pattern dictionary through local updates, consensus building, and optimization, using a blockchain structure to store and update predictive models, while ensuring data privacy by encrypting client-node communications and randomly distributing local statistics among consensus nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional cryptographic methods are used to protect noisy multivariate real-valued data from IoT devices, then data security is improved, but computational overhead and processing efficiency deteriorate
Solution Approach 1:
The patent segments the data protection mechanism into two parts: (1) transformation of noisy multivariate real-valued data into discrete symbolic sequences using a pattern dictionary, and (2) application of cryptographic methods only on the transformed symbolic data. This segmentation reduces computational overhead while maintaining security, as the transformation step eliminates the need for encryption on raw sensor data.
Solution Approach 2:
The pattern dictionary serves as an intermediary that transforms continuous noisy data into discrete symbolic representations. This intermediary structure enables subsequent cryptographic operations to be performed more efficiently on the symbolic data rather than on the original high-precision real-valued data, thus reducing computational overhead while preserving security.
2Reliability
If encryption-based multiparty computation is used for secure collaborative learning, then data privacy is improved, but processing speed and efficiency deteriorate
Solution Approach 1:
The patent changes the parameter representation of data from continuous real-valued measurements to discrete symbolic sequences. This parameter transformation enables collaborative learning algorithms to operate on symbolic data with reduced computational complexity, improving processing speed while maintaining privacy through the symbolic abstraction layer.
Solution Approach 2:
The collaborative learning process is segmented into: (1) local transformation of sensor data into symbolic sequences using pattern dictionaries, (2) exchange and aggregation of symbolic statistics among participants, and (3) centralized model updating. This segmentation avoids the need for continuous encryption operations during data exchange, thereby improving processing speed while preserving privacy.
3Measurement precision
If raw sensor data is shared among multiple client systems for collaborative learning, then model accuracy is improved, but data security and privacy deteriorate
Solution Approach 1:
The patent extracts only the essential statistical features from raw sensor data and transforms them into symbolic sequences for sharing among client systems. By taking out only the necessary information (symbolic statistics) rather than sharing complete raw data, the system achieves collaborative learning with improved model accuracy while maintaining data security and privacy.
Solution Approach 2:
Instead of sharing original raw sensor data, the system creates symbolic copies or representations of the data through pattern dictionary transformation. These symbolic copies preserve the essential patterns and statistics needed for collaborative learning while being computationally efficient and secure to transmit, thus improving model accuracy without compromising data security.
Data Source
AI summary
A collaborative learning framework is presented. The collaborative framework is implemented by multiple network nodes interconnected by a network. The network nodes belong to multiple client systems of the framework. A network node belonging to a first client system constructs a predictive model for the first client system by using a pattern dictionary that is a built based on a consensus among the multiple client systems. The network node calculates a set of local statistics for the first client system based on raw data of the first client system. The network node computes a consensus set of local statistics by aggregating sets of local statistics from the multiple client systems. The network node updates the pattern dictionary based on current values of the pattern dictionary and the consensus set of local statistics.