Privacy-Preserving Interpretable Skill Learning for Healthcare
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Solution Overview
Problem
Imitation learning systems lack intrinsic interpretability and face challenges in sharing expert demonstrations across different data silos, especially in privacy-sensitive applications like finance and healthcare due to regulatory and practical limitations.
Innovation Solution
A method for training a healthcare treatment machine learning model that aggregates local weights from clients to update global weights for an imitation-based skill learning model, clusters local prototype vectors, selects representative vectors, and distributes updated weights and client-specific prototype vectors to maintain interpretability and privacy preservation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If imitation learning systems are used to replicate expert skills, then decision-making capability is improved, but interpretability deteriorates making the learned policy difficult to trust
Solution Approach 1:
The patent introduces prototype vectors as intermediary elements that bridge the gap between the imitation learning model and human interpreters. These prototypes serve as mediators that capture essential patterns from expert demonstrations while maintaining explicit, human-understandable representations. The prototypes act as a intermediary layer that preserves interpretability information that would otherwise be lost in the black-box learning process.
Solution Approach 2:
The patent transforms the abstract, invisible internal representations of the imitation learning model into visible, interpretable prototype vectors that can be observed and understood. This is analogous to making invisible processes visible - the prototypes provide a 'colorful' or distinguishable representation of the learned skills that humans can interpret, rather than relying on opaque numerical weights.
2Adaptability or versatility
If expert demonstrations are shared across different data silos to improve learning, then skill knowledge is enhanced, but privacy is compromised due to regulatory and practical limitations
Solution Approach 1:
The patent extracts only the essential skill patterns from expert demonstrations in the form of prototype vectors, rather than sharing the complete raw demonstration data. This extraction process separates the useful skill knowledge from the sensitive personal or proprietary information contained in the original demonstrations, allowing knowledge sharing while preserving privacy.
Solution Approach 2:
The patent creates simplified copies of expert skills in the form of prototype vectors that capture the essential patterns without containing the original sensitive data. These prototype copies can be shared across data silos and federated systems, providing the benefits of knowledge transfer while maintaining the privacy and security of the source data through lossless or lossy compression techniques.
3Reliability
If local data is kept private in federated learning to preserve privacy, then data security is improved, but model performance deteriorates due to limited local data
Solution Approach 1:
The patent merges the strengths of both centralized and federated approaches by combining local prototype extraction with global prototype aggregation. Each client locally extracts prototypes from their private data, then these local prototypes are combined with global prototypes in a federated manner. This merging allows the model to benefit from both local data security and global knowledge sharing, improving performance without compromising privacy.
Solution Approach 2:
The patent moves the learning process to a different dimension by working with prototype vectors rather than raw data. This dimensional transformation allows privacy-preserving knowledge sharing because prototypes exist in a compressed, abstract space that separates useful information from sensitive details, enabling federated learning with improved performance while maintaining data security.
Data Source
AI summary
Methods and systems for training a healthcare treatment machine learning model include aggregating local weights from a set of clients to update a set of global weights for an imitation-based skill learning model. A set of local prototype vectors are clustered from the plurality of clients to generate clusters. Representative vectors are selected for the clusters as a set of global prototypes. Client-specific prototype vectors are determined for the clients based on the representative vectors. The updated set of global weights and the client-specific prototype vectors are distributed to the clients.