Federated Learning Model with Domain-Specific Weights
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Solution Overview
Problem
Training artificial intelligence and machine learning models on non-iid or heterogeneous data is challenging due to data heterogeneity across different domains in healthcare and other industries, where economic, regulatory, and privacy concerns complicate the accumulation and sharing of data, especially in environments where data is not independent and identically distributed.
Innovation Solution
Implementing a method that uses both global and domain-specific weights in machine learning models, where global weights are shared across domains and domain-specific weights are tailored to each domain, allowing for the transformation of domain-specific data into a normalized, domain-independent form without requiring data sharing, using techniques like federated learning and sparse ternary compression to facilitate training and inference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is accumulated from heterogeneous sources to improve model training, then model accuracy is improved, but data privacy and security risks increase
Solution Approach 1:
The patent segments the model into multiple independent components or modules, each processing specific aspects of the data. This allows different data sources to contribute to different segments without requiring centralized access to all data, thereby improving model accuracy through diverse data input while maintaining privacy by preventing any single point from accessing the complete dataset.
Solution Approach 2:
The patent introduces intermediary components that act as mediators between heterogeneous data sources and the core model. These intermediaries process and transform data locally before transmission, enabling model training on diverse data while reducing privacy risks by preventing direct exposure of raw sensitive data to the central system or other domains.
2Measurement precision
If data is centralized for model training, then model training effectiveness is improved, but data exchange burden and communication overhead increase
Solution Approach 1:
The patent implements local quality by allowing each domain or data source to maintain and process its own data locally with domain-specific optimizations. This enables effective model training using local data characteristics without requiring extensive data exchange, thereby reducing communication overhead while maintaining training effectiveness through localized adaptation.
Solution Approach 2:
The patent performs preliminary data processing, filtering, and transformation actions at the data source before transmission. This preliminary action reduces the volume and complexity of data that needs to be exchanged, thereby lowering communication burden while ensuring that the data received by the model is already optimized for training effectiveness.
3Adaptability or versatility
If domain-specific data processing is implemented, then adaptability to local domains is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal framework that can handle multiple domains through a common architecture. This universal system provides domain-specific adaptability through configurable modules or parameters that can be adjusted for different domains without requiring completely separate systems, thereby maintaining domain adaptability while controlling overall system complexity through reuse of core components.
Data Source
AI summary
Techniques described herein relate to training artificial intelligence and machine learning models on non-iid or heterogeneous data, for adapting previously-trained models to new data sources, and for using these models to make inferences. In various embodiments, data may be obtained from one or more data sources that are available in a given domain. The data may be in a domain-specific form that is specific to the given domain. The data may be processed using one or more trained machine learning models. The one or more trained machine learning models may include: a domain-specific set of weights that is tailored to the given domain, and a global set of weights that is shared across a plurality of domains of a federated learning system. An outcome of the process may be provided at one or more output components.