Federated Learning for Privacy-Preserving Edge Anomaly Detection
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Solution Overview
Problem
Existing machine learning models face challenges in anomaly detection in cyber-physical systems due to data privacy concerns and heterogeneity of data across edge devices, limiting the effectiveness of centralized training.
Innovation Solution
The method employs federated learning by collecting model exemplar information from edge devices, aggregating it using federated averaging, and training global model exemplars using federated constrained clustering, allowing for anomaly detection without sharing sensitive local data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is shared centrally for training, then model training effectiveness is improved, but data privacy security deteriorates
Solution Approach 1:
The system segments the training process into local and central components. Local models are trained independently on edge devices using local data, while only model parameters (not raw data) are shared with the central server for aggregation. This segmentation allows effective training while preserving data privacy at the edge devices.
Solution Approach 2:
The patent introduces model parameters as an intermediary between local data and central training. Instead of sharing raw data directly, the system uses trained model parameters from edge devices as intermediaries that capture local data characteristics without exposing the actual data, thus enabling effective training while maintaining privacy security.
2Measurement precision
If centralized training is used, then model accuracy is improved, but system complexity increases
Solution Approach 1:
The training system is segmented into distributed local models and a central aggregation server. Each edge device maintains its own model trained on local data, reducing the computational burden on any single system. The central server only performs parameter aggregation, simplifying the overall system architecture while maintaining high detection accuracy.
Solution Approach 2:
The system uses copies of model parameters from multiple edge devices to train the central model. Instead of requiring a single complex centralized system, multiple simplified local models are copied and their parameters are aggregated, achieving high accuracy through ensembling while reducing individual system complexity.
3Object-affected harmful factors
If local data is kept private, then data privacy is improved, but model training effectiveness deteriorates
Solution Approach 1:
The patent extracts only the essential training information from local data in the form of model parameters. These parameters capture the essential patterns from local data without exposing the data itself. The extracted parameters are then shared for aggregation, enabling effective training while maintaining data privacy through selective extraction of only necessary information.
Solution Approach 2:
Model parameters serve as an intermediary that bridges local data privacy requirements with central training effectiveness. The parameters act as a compressed representation of local data characteristics, allowing the central server to learn from diverse data distributions without accessing the actual data, thus maintaining both privacy and training effectiveness.
4Adaptability or versatility
If heterogeneous data is handled, then adaptability is improved, but data homogeneity deteriorates
Solution Approach 1:
The system applies local quality by allowing each edge device to train its model on its own local data with specific characteristics. Each local model adapts to its unique data distribution and operational context. The central server then aggregates these diverse local models, preserving their individual characteristics while achieving overall system adaptability to heterogeneous data sources.
Solution Approach 2:
The patent embraces asymmetry in data distributions across different edge devices. Instead of requiring homogeneous data, the system allows each device to have its own asymmetric data characteristics. The federated averaging process handles these asymmetric distributions by weighting and combining parameters from diverse sources, maintaining data homogeneity in the aggregated model while preserving adaptability to heterogeneous local conditions.
Data Source
AI summary
Methods and systems for training a neural network include collecting model exemplar information from edge devices, each model exemplar having been trained using information local to the respective edge devices. The collected model exemplar information is aggregated together using federated averaging. Global model exemplars are trained using federated constrained clustering. The trained global exemplars are transmitted to respective edge devices.