Physics-Enhanced Federated Graphs for Private Multi-Species Analysis

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Solution Overview

Problem

Current distributed computing systems lack the ability to securely coordinate large-scale genomic interventions across multiple institutions while maintaining data privacy, particularly in biological research, and fail to adapt to varying computational demands and privacy requirements, leading to fragmented solutions that limit complex analyses and modeling capabilities.

Innovation Solution

A federated distributed computational graph (FDCG) architecture that integrates physics-based modeling, quantum HPC resources, and advanced cryptography to enable secure cross-institutional collaboration, supporting multi-scale biological analysis with real-time optimization and privacy preservation through components like local computational engines, privacy preservation subsystems, and communication interfaces.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If centralized data architecture is used to enable complex biological analysis, then analytical capability is improved, but data privacy and security are compromised

Engineering Contradiction:

Improvebiological analysis capabilityVSAvoiddata privacy risk

Core Design Contradiction:

ProductivityVSObject-affected harmful factors

Solution Approach 1:

The system segments the centralized computational graph into distributed sub-graphs across multiple institutional nodes. Each node processes local biological data independently while the federation manager coordinates cross-node computations, enabling complex multi-species analysis without centralizing sensitive data.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The federation manager acts as an intermediary between distributed computational nodes, coordinating task execution and data exchange without accessing the actual biological data. It manages the global computational graph topology and schedules computations while preserving institutional data privacy boundaries.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Stability of the object's composition

If rigid operational framework is imposed to maintain system control, then system stability is improved, but adaptability to varying computational demands is reduced

Engineering Contradiction:

Improvesystem controlVSAvoidcomputational demand adaptability

Core Design Contradiction:

Stability of the object's compositionVSAdaptability or versatility

Solution Approach 1:

The computational graph topology and task allocation are dynamically adjusted based on real-time computational demands and resource availability. The federation manager can reconfigure the distributed system architecture, add or remove nodes, and modify task schedules without disrupting overall system stability or control.

Inventive Principle:

Principle #15Dynamics

3Productivity

If data centralization is implemented to enable real-time optimization, then optimization capability is improved, but security vulnerabilities increase

Engineering Contradiction:

Improvereal-time optimization capabilityVSAvoidsystem security

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system segments real-time optimization computations into distributed operations across multiple nodes. Each node performs local optimization on its data subset while the federation manager coordinates global optimization objectives, achieving real-time optimization without creating a centralized security vulnerability.

Inventive Principle:

Principle #1Segmentation

4Object-affected harmful factors

If advanced cryptography is integrated to preserve privacy, then data protection is improved, but computational complexity increases

Engineering Contradiction:

Improvedata protection levelVSAvoidsystem complexity

Core Design Contradiction:

Object-affected harmful factorsVSDevice complexity

Solution Approach 1:

The system applies cryptography selectively rather than universally - using encryption and secure protocols only for data transmission and specific sensitive operations, while leaving local computational processing uncrypted for efficiency. This partial application of cryptographic measures balances protection with computational simplicity.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20250259711A1Physics-enhanced federated distributed computational graph architecture for multi-species biological system engineering and analysis

Publication Date: 2025.08.14 QOMPLX INC

AI summary

A federated distributed computational system enables secure collaboration across multiple institutions for multi-species biological data analysis. The system consists of interconnected computational nodes managed by a central federation manager. Each node contains specialized components that work together to process multi-species biological data while preserving privacy. These components include a local computational engine that handles data processing, a physics-information integration subsystem that combines physical state calculations with information-theoretic optimization, a privacy preservation module that protects sensitive information, a knowledge integration component that manages biological data relationships, and a communication interface that enables secure information exchange between nodes. The federation manager coordinates all computational activities and manages resource allocations across the network while ensuring data privacy is maintained throughout the process. This architecture allows research institutions to collaboratively analyze complex, multi-species biological systems through integrated physics-based modeling and information-theoretic approaches while maintaining security and confidentiality.