Distributed AI Subtask Assignment for Latency and Privacy
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Solution Overview
Problem
Conventional AI systems face performance and security challenges due to their complexity, which leads to data latency and privacy concerns when using cloud-based processing, especially when handling sensitive data.
Innovation Solution
The described techniques enable subtask assignment for AI tasks across multiple local client devices within a local computing environment, distributing the workload based on device capabilities, availability, and task complexity, while minimizing data exposure by limiting transmission outside the local network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If cloud-based AI processing is used, then computational power and processing speed are improved, but data latency and security risks increase
Solution Approach 1:
The patent segments the AI computation task into multiple subtasks and distributes them across different client devices in the local network. Each device processes its assigned subtask locally, eliminating the need for complete data round-trips to the cloud and reducing latency while maintaining distributed computational power.
Solution Approach 2:
The patent transitions from a single centralized cloud processing model to a multi-dimensional distributed local network model. By adding the spatial dimension of local network distribution, the system achieves both high computational power through multiple devices and low latency through local processing, resolving the contradiction between centralized power and distributed speed.
2Power
If cloud-based AI processing is used, then computational power is improved, but data security and privacy protection deteriorate
Solution Approach 1:
The patent segments both the computational task and the data processing responsibilities across multiple local devices. Sensitive data remains within the local network and is processed by individual devices rather than being transmitted to external cloud systems, reducing privacy risks while maintaining distributed computational power.
Solution Approach 2:
The local network acts as an intermediary between the user's data and external cloud systems. By processing AI subtasks within the local network boundary, the system mediates data protection by preventing sensitive information from leaving the controlled local environment while still leveraging distributed computational resources.
3Object-affected harmful factors
If AI tasks are distributed across multiple local devices, then data security is improved, but system complexity increases
Solution Approach 1:
The system implements self-service mechanisms where client devices automatically discover available subtasks and their own capabilities, then autonomously select and execute appropriate tasks. This self-organizing behavior reduces the need for complex centralized management and configuration, making the distributed system easier to deploy while maintaining security through local processing.
4Productivity
If AI tasks are distributed across multiple local devices, then resource utilization is improved, but task coordination complexity increases
Solution Approach 1:
The system implements feedback mechanisms where devices report their task completion status, resource availability, and processing results back to the network. This feedback enables dynamic task allocation and load balancing, optimizing resource utilization across devices while the standardized feedback protocol keeps coordination complexity manageable through automated rather than manual management.
Data Source
AI summary
Techniques for subtask assignment for an artificial intelligence (AI) task are described, and may be implemented to leverage a local set of devices to distribute portions of an AI task between the devices. Generally, the described techniques enable AI task allocation based on a variety of factors, such as device capabilities, device availability, task complexity, and so forth.