20240020576. METHODS, SYSTEMS, AND FRAMEWORKS FOR FEDERATED LEARNING WHILE ENSURING BI DIRECTIONAL DATA SECURITY simplified abstract (BioSymetrics, Inc.)
Contents
- 1 METHODS, SYSTEMS, AND FRAMEWORKS FOR FEDERATED LEARNING WHILE ENSURING BI DIRECTIONAL DATA SECURITY
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHODS, SYSTEMS, AND FRAMEWORKS FOR FEDERATED LEARNING WHILE ENSURING BI DIRECTIONAL DATA SECURITY - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Original Abstract Submitted
METHODS, SYSTEMS, AND FRAMEWORKS FOR FEDERATED LEARNING WHILE ENSURING BI DIRECTIONAL DATA SECURITY
Organization Name
Inventor(s)
Nishanth Merwin of Toronto (CA)
METHODS, SYSTEMS, AND FRAMEWORKS FOR FEDERATED LEARNING WHILE ENSURING BI DIRECTIONAL DATA SECURITY - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240020576 titled 'METHODS, SYSTEMS, AND FRAMEWORKS FOR FEDERATED LEARNING WHILE ENSURING BI DIRECTIONAL DATA SECURITY
Simplified Explanation
The patent application relates to methods, systems, and frameworks for data analytics using machine learning. Specifically, it focuses on preprocessing biomedical data on a parallel cloud computing network while ensuring bi-directional data security. The system includes a processor for storing and running a biomedical predictive model that processes proprietary data. It also includes an administrative account for controlling the parallel cloud computing network and proprietary data, as well as multiple other accounts for accessing the network. A network facilitates the transportation of the biomedical predictive model and proprietary data while ensuring bi-directional data security.
- The patent application describes methods, systems, and frameworks for data analytics using machine learning.
- It focuses on preprocessing biomedical data on a parallel cloud computing network while ensuring bi-directional data security.
- The system includes a processor for storing and running a biomedical predictive model that processes proprietary data.
- An administrative account is provided to control the parallel cloud computing network and proprietary data.
- Multiple other accounts are configured to access the parallel computing network.
- A network facilitates the transportation of the biomedical predictive model and proprietary data while ensuring bi-directional data security.
Potential Applications
This technology has potential applications in various fields, including:
- Biomedical research: The preprocessing of biomedical data using machine learning can help researchers gain insights and make predictions in areas such as genomics, drug discovery, and personalized medicine.
- Healthcare: By analyzing biomedical data, this technology can assist in diagnosing diseases, monitoring patient health, and predicting treatment outcomes.
- Pharmaceutical industry: The use of machine learning in preprocessing biomedical data can aid in drug development, clinical trials, and pharmacovigilance.
Problems Solved
This technology addresses several problems in the field of data analytics and machine learning, including:
- Preprocessing complexity: Preprocessing biomedical data can be challenging due to its complexity and size. This technology provides a framework for efficiently processing and analyzing such data on a parallel cloud computing network.
- Data security: Ensuring bi-directional data security is crucial when dealing with proprietary biomedical data. This technology addresses this concern by implementing measures to protect the data during transportation and processing.
- Scalability: The use of a parallel cloud computing network allows for scalability, enabling the processing of large volumes of biomedical data in a timely manner.
Benefits
The use of this technology offers several benefits, including:
- Improved data analysis: By leveraging machine learning techniques, this technology enhances the analysis of biomedical data, leading to more accurate predictions and insights.
- Enhanced data security: The implementation of bi-directional data security measures ensures the protection of proprietary biomedical data throughout its transportation and processing.
- Increased efficiency: The use of a parallel cloud computing network allows for faster and more efficient preprocessing of biomedical data, enabling timely analysis and decision-making.
Original Abstract Submitted
some embodiments relate to methods, systems, and frameworks for data analytics using machine learning, such as methods and systems for preprocessing of biomedical data on a parallel cloud computing network while ensuring bi-directional data security. the system may include a processor that is configured to store and run a biomedical predictive model that processes proprietary data. the system may also include an administrative account that is configured to control the parallel cloud computing network and proprietary data, plurality of other accounts that are configured to access to the parallel computing network and a network that facilitates the transportation of the biomedical predictive model as well as proprietary data while ensuring bi-directional data security.
