Federated Learning Data Corruption Detection via RNN
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Solution Overview
Problem
Traditional federated machine-learning models are vulnerable to data corruption due to missing or intentionally/ unintentionally altered data, leading to inefficiencies in identifying and addressing corrupted data instances, which can be costly and time-consuming.
Innovation Solution
Implement a system that groups client devices to apply a federated machine-learning model, trains recurrent neural networks to derive low-dimensional representations of model updates, calculates data quality scores, classifies data instances as clean or corrupt, and performs security actions to protect against malicious data by tagging potentially corrupt instances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional federated machine-learning models are used to train distributed data analytics, then data privacy is maintained through local processing, but the models become vulnerable to data corruption from missing or intentionally altered data instances
Solution Approach 1:
The system performs preliminary actions by training a detection model in advance to identify corrupted data instances before they affect the main federated learning training process. The detection model is trained using labeled corrupted and clean data instances, enabling it to proactively flag suspicious data before it contaminates the global model updates.
Solution Approach 2:
The patent introduces an intermediary detection model that acts as a mediator between local client devices and the central server. This intermediary layer analyzes model updates and data instances to identify corruption, preventing harmful data from propagating through the federated learning system while maintaining the privacy-preserving architecture.
2Measurement precision
If manual identification of corrupted data instances is performed, then data quality can be assessed, but the process becomes expensive and time-consuming
Solution Approach 1:
The system implements self-service by enabling automatic detection and classification of corrupted data instances through the trained detection model. The model autonomously analyzes model updates and data instances, eliminating the need for manual inspection while maintaining high accuracy in identifying corruption. This automated approach significantly reduces both time and computational costs.
Solution Approach 2:
The patent replaces manual mechanical inspection processes with an automated machine learning-based detection system. The detection model uses learned patterns from training data to automatically identify corrupted instances, substituting human effort with an efficient computational process that scales to handle large volumes of federated learning data.
3Productivity
If all data instances from client devices are collected for training, then comprehensive model training is achieved, but the system becomes vulnerable to adversarial data poisoning and random data corruption
Solution Approach 1:
The system extracts and removes harmful elements by identifying and excluding corrupted data instances from the training process. The detection model analyzes each model update and data instance to separate clean data from corrupted data, extracting only the beneficial information for training while discarding malicious or corrupted contributions from client devices.
Solution Approach 2:
The patent converts the presence of corrupted data into a benefit by using it as training material for the detection model. Corrupted data instances, when properly labeled, serve to teach the detection model what corruption looks like, enabling it to identify and reject similar instances in future training rounds. This transforms a harmful element into a useful training resource.
Data Source
AI summary
The disclosed computer-implemented method for utilizing federated machine-learning to protect against potentially malicious data may include (i) arranging a set of client devices into groups for applying a federated machine-learning model, (ii) determining model updates for each of the groups over a predetermined period, (iii) training one or more recurrent neural networks to derive a low-dimensional representation of the model updates, (iv) calculating a data quality score for each of the client devices based on the model updates, (v) applying the federated machine-learning model to classify data instances on each of the client devices as including clean data or potentially corrupt data, and (vi) performing a security action that protects against the potentially malicious data by tagging the data instances classified as the potentially corrupt data. Various other methods, systems, and computer-readable media are also disclosed.