Collaborative Learning Model Aggregation Against Malicious Updates
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Solution Overview
Problem
Collaborative learning systems face challenges in identifying and preventing malicious attacks on global models due to the inability of central servers to monitor training processes for data privacy reasons, leading to compromised model robustness and accuracy.
Innovation Solution
A clustering-based adaptive robust collaborative learning method that utilizes trusted global model parameters as a trust guidance for parameter-level clustering, determines trusted local models, and assigns weights based on similarity to update the global model, enhancing model robustness by distinguishing benign from malicious models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If collaborative learning is implemented to solve data islands, then data utilization and model adaptability are improved, but the system becomes vulnerable to malicious attacks and data poisoning
Solution Approach 1:
The patent introduces a central server as an intermediary that aggregates local models from multiple collaborators and performs clustering analysis to identify malicious models. This intermediary structure enables coordinated defense against attacks while preserving the distributed collaborative learning framework, resolving the contradiction between maintaining adaptability through distributed learning and ensuring robustness through centralized coordination.
Solution Approach 2:
The patent replaces traditional mechanical monitoring methods with algorithmic clustering analysis. Instead of directly monitoring training processes (which would violate privacy), the system uses unsupervised clustering algorithms to detect anomalies in model parameters, substituting physical monitoring with computational pattern recognition to maintain both privacy and security.
2Reliability
If the central server monitors training processes to detect malicious models, then model robustness is improved, but data privacy is compromised
Solution Approach 1:
The patent extracts only the necessary model parameters for aggregation and clustering analysis, rather than monitoring complete training processes or raw data. By taking out only the essential model updates needed for security verification, the system achieves robustness detection while minimizing privacy loss and maintaining the confidential nature of local training data.
Solution Approach 2:
The patent transforms the monitoring approach from observing training processes to analyzing model parameter distributions. By changing the observation target from process-level information to parameter-level statistics, the system enables security monitoring without accessing sensitive training data, thus preserving privacy while detecting malicious models through parameter clustering.
3Productivity
If traditional aggregation methods are used to combine local models, then computational efficiency is improved, but the system becomes susceptible to adversarial attacks
Solution Approach 1:
The patent performs clustering analysis on model parameters before performing the final aggregation operation. This preliminary action identifies and isolates malicious models that would otherwise poison the aggregated result, allowing the system to maintain computational efficiency by using simple aggregation only on verified benign models while defending against adversarial attacks.
Solution Approach 2:
The patent converts the harmful effect of malicious model submissions into a beneficial detection opportunity. By using clustering analysis on aggregated parameters, the system identifies outliers caused by adversarial attacks and can either exclude them or use their distinctive patterns to improve detection, thereby transforming the security threat into a detection advantage.
Data Source
AI summary
A clustering-based adaptive robust collaborative learning method includes: local models uploaded by a plurality of collaborative terminals and at least part of training data of the plurality of the collaborative terminals for training the local models are obtained, a trusted global model is trained based on the at least part of the training data; by taking trusted global model parameters as a trust guidance, a clustering operation is performed on each parameter dimension of the local models based on a predetermined optimal clustering algorithm respectively; and weights of the trusted local model parameters are determined based on similarities between the trusted global model parameters and the trusted local model parameters, to update the trusted global model parameters.