Decentralized Autoencoder Anomaly Detection Pipeline
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Solution Overview
Problem
Decentralized machine learning approaches face challenges in detecting anomalies and data drift across distributed nodes, leading to inaccurate model performance and potential malicious data propagation, especially when data distribution is not IID, and existing solutions lack accuracy and visibility in decentralized environments.
Innovation Solution
A pipeline utilizing local and global variational autoencoders (VAEs) for anomaly and drift detection, where local VAEs are trained on individual node data and global VAEs across nodes, enabling effective detection and assessment of differences in latent space distributions, with clustering and statistical ensemble methods to identify outliers and changing data patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If decentralized learning is implemented without central data repository, then data privacy and security are improved, but anomaly detection accuracy deteriorates
Solution Approach 1:
The patent introduces autoencoders as intermediary models that learn normal data patterns without accessing raw data. These autoencoders act as mediators between the decentralized nodes and the anomaly detection process, enabling accurate anomaly detection while preserving data privacy through their reconstruction capability that compares original vs. reconstructed data at local level
Solution Approach 2:
The patent creates copies of normal training data patterns through autoencoder models at each decentralized node. Instead of sharing sensitive raw data, nodes share reconstructed versions or anomaly scores generated by local autoencoders, maintaining detection accuracy while preventing data leakage through the use of these data copies
2Adaptability or versatility
If data distribution is not IID across nodes, then adaptability to diverse data sources is improved, but model training stability deteriorates
Solution Approach 1:
The patent applies local quality by training separate autoencoders at each node that are adapted to local data characteristics而非 forcing a single global model. Each node's autoencoder learns local normal patterns specific to its data distribution, allowing the system to handle heterogeneous data sources while maintaining local training stability through node-specific adaptation
3Reliability
If local autoencoders are trained independently without global reference, then data privacy is improved, but drift detection capability deteriorates
Solution Approach 1:
The patent implements feedback by having nodes share anomaly scores and drift metrics generated by their local autoencoders with the central server. The server aggregates this feedback from multiple nodes to detect global data drift patterns, enabling drift detection capability while maintaining data privacy since only aggregated statistical feedback is shared, not raw data
Data Source
AI summary
Anomalies and drift detection in decentralized learning environments. The method includes deploying at a first node, (1) a local unsupervised autoencoder, trained at the first node, along with a local training data reference baseline for the first node, and (2) a global unsupervised autoencoder trained across a plurality of nodes, along with a corresponding global training data reference baseline. Production data at the first node is processed with local and global ML models deployed by a user. At least one of local and global anomaly data regarding anomalous production data or local and global drift data regarding drifting production data is derived based on the local and global training data reference baselines, respectively. At least one of the local anomaly data is compared with the global anomaly data or the local drift data with the global drift data for assessing impact of anomalies/drift on the ML models.