Decentralized IID Checking Mechanism for Swarm Learning Data Drift
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Solution Overview
Problem
Conventional machine learning systems lack provisions to detect independent and identically distributed (IID) data, leading to spurious weights and reduced trustworthiness of models due to non-IID data, which can cause data drift and concept shifts, affecting the reliability of decentralized models.
Innovation Solution
Implementing a decentralized IID checking mechanism within the Swarm Learning framework using blockchain technology to isolate and correct non-conforming data batches before training, ensuring data conformance to IID norms through explicit data configuration checks and statistical analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If decentralized machine learning is implemented without IID checking, then data privacy and security are improved, but model reliability and trustworthiness deteriorate due to non-IID data causing data drift
Solution Approach 1:
The patent implements preliminary IID checking of data batches before they are used for model training. The system checks whether incoming data batches conform to IID requirements and isolates non-conforming batches before they can affect the training process. This preliminary action prevents data drift from compromising model reliability while maintaining the decentralized architecture.
Solution Approach 2:
The patent introduces an intermediary IID checking mechanism that acts as a mediator between data sources and the training process. This intermediary layer verifies data conformance without requiring centralization, allowing the system to maintain privacy while ensuring data quality through statistical analysis and isolation of non-IID batches.
2Reliability
If IID checking mechanism is implemented, then data conformance and model reliability are improved, but computational overhead and processing time increase
Solution Approach 1:
The patent applies partial checking by focusing only on the essential IID requirements rather than comprehensive data validation. The system checks specific statistical properties necessary for IID conformance without performing exhaustive analysis on every data point, thereby reducing processing time while maintaining adequate conformance verification.
Solution Approach 2:
The patent utilizes statistical parameter analysis to efficiently detect non-IID data batches. By monitoring key statistical parameters and comparing them against expected distributions, the system可以快速 identify conforming and non-conforming batches without time-consuming comprehensive validation, balancing reliability with processing efficiency.
Data Source
AI summary
Systems and methods for checking whether training data to be inputted into a training phase of a ML model is Independent and Identically Distributed data (IID data), and taking action based on that determination. One example of the present disclosure provides a method implemented by an edge node operating in a distributed swarm learning blockchain network. The method includes receiving a smart contract including a definition of conforming data and executing the smart contract including the definition of conforming data. The method further includes receiving one or more batches of training data for training a ML model. The method further includes checking whether each batch of training data conforms to the agreed-upon definition of conforming data, tagging and isolating non-conforming batches of training data, and inputting conforming batches of training data into a training phase of the machine learning model. The conforming batches of training data are IID data.