Federated Distributed Computational Graphs for Private AI Orchestration
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Solution Overview
Problem
Current AI systems, particularly large language models (LLMs), face challenges in managing complex and heterogeneous computing environments across diverse counterparties, requiring advanced resource allocation and privacy-preserving orchestration while addressing cybersecurity, intellectual property, and regulatory concerns, especially in federated distributed computing scenarios.
Innovation Solution
A federated distributed graph-based computing platform that utilizes a cloud-based architecture for neuro-symbolic reasoning, enabling flexible and scalable integration of machine learning and simulation models across heterogeneous environments, with decentralized execution and privacy-preserving data flows, supported by a federated distributed computational graph (DCG) system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If federated distributed computing is used across heterogeneous environments, then adaptability and versatility are improved, but device complexity and orchestration difficulty increase
Solution Approach 1:
The system segments the federated computing environment into discrete computational graphs that can be independently managed, distributed, and executed across heterogeneous nodes. Each computational graph represents a modular unit of computation that can be composed and orchestrated without managing the entire distributed system as a monolithic complex structure.
Solution Approach 2:
The patent introduces an intermediary layer that handles the orchestration and coordination of computational graphs across federated nodes. This intermediary manages the complexity of distributed execution, resource allocation, and privacy-preserving data flows, shielding users from the underlying system complexity while enabling adaptability across heterogeneous environments.
2Reliability
If privacy-preserving data flows are implemented, then information security is improved, but system performance and data accessibility may deteriorate
Solution Approach 1:
The system extracts sensitive data from the computation process by using computational graphs that operate on encrypted or anonymized data representations. Privacy-preserving techniques are extracted as separate layers that wrap the core computational logic, allowing security enhancements without fundamentally altering the performance-critical computation paths.
Solution Approach 2:
The patent employs copying mechanisms where computational graphs are replicated and distributed across federated nodes, each operating on local or encrypted data copies. This allows parallel processing and maintains performance while preserving privacy, as no single node needs access to the complete unencrypted dataset.
3Reliability
If decentralized execution is implemented, then system reliability and fault tolerance are improved, but coordination overhead and communication costs increase
Solution Approach 1:
The system performs preliminary actions by pre-compiling and validating computational graphs before distribution to federated nodes. Dependency relationships and execution orders are predetermined and embedded in the graph structure, reducing the need for real-time coordination and communication overhead during actual execution, while maintaining decentralized reliability.
Data Source
AI summary
A federated distributed AI reasoning and action platform utilizing decentralized, partially observable hierarchical computing for neuro-symbolic reasoning. It features a federated Distributed Computational Graph (DCG) system integrating core components like pipeline orchestration, transformers, and marketplaces. The platform enables privacy-preserving dynamic resource allocation, intelligent task scheduling, and variable information sharing across diverse computing environments. By coordinating with an AI-based operating system and analyzing performance metrics, environmental conditions, and resource availability, the system optimizes efficiency across AI workloads and decision-making processes. This results in an adaptive, power-efficient, and scalable AI-enabled data processing system capable of handling complex tasks while maintaining peak performance under various operating conditions.