AI Model Steering Routes for Privacy-Aware Distributed Training
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Solution Overview
Problem
Existing AI model training methods face challenges such as network congestion, resource inefficiency, data privacy concerns, and model drift due to fluctuating network conditions and complex neural network splitting, which affect training performance and efficiency.
Innovation Solution
A system and method for training AI models using a network with node clusters, employing a control plane and AI model steering apparatus (MTRCM) to adaptively route the model through nodes based on current state information, including resource availability, data quality, and trustworthiness, ensuring efficient and secure training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning is used to train AI models using distributed data, then data privacy is preserved, but network congestion and communication overhead increase
Solution Approach 1:
The system pre-calculates and stores routing information for multiple paths between nodes before training begins. When training occurs, the pre-computed routes enable immediate data transmission without real-time routing calculations, reducing communication overhead and network congestion while preserving data privacy through federated learning
Solution Approach 2:
The routing system dynamically selects different paths based on current network conditions, node availability, and data requirements. This dynamic route selection optimizes communication efficiency by avoiding congested paths while maintaining the privacy benefits of federated learning through adaptive data transmission
2Ease of operation
If sequential learning is used to train AI models with new data streams, then model updating is simplified, but training performance and efficiency decrease due to overlooked data parameters
Solution Approach 1:
The system incorporates feedback mechanisms that continuously monitor data quality parameters including reliability, relevance, size, and age. This feedback loop enables the system to automatically adjust training processes and select optimal data streams, improving training efficiency while maintaining the simplicity of sequential model updating through automated parameter evaluation
Solution Approach 2:
The system dynamically evaluates and adjusts data parameters such as reliability scores, relevance weights, size thresholds, and age limits during training. By changing these parameters based on current training needs and data characteristics, the system optimizes training efficiency while preserving the operational simplicity of sequential learning approaches
3Reliability
If split learning is used to process AI model layers locally, then raw data privacy is protected, but network resource accountability is lost and training time increases
Solution Approach 1:
The system pre-establishes accounts and resource allocation plans for each node before split learning begins. These pre-configured resource accounts track and manage computational resources across distributed nodes, enabling efficient coordination of local model processing while maintaining privacy protection and reducing training time through proactive resource management
Solution Approach 2:
The system introduces an intermediary accounting mechanism that coordinates resource allocation and tracking across distributed nodes performing split learning. This intermediary layer maintains network resource accountability while allowing local processing to proceed, optimizing the balance between privacy protection and training efficiency through centralized resource management
4Ease of operation
If centralized AI model training is used to share training data, then model training is simplified, but data privacy is compromised and network resource accountability is reduced
Solution Approach 1:
The system segments the centralized training process into distributed node operations, where each node processes local data independently while contributing to the overall model training. This segmentation maintains training simplicity through coordinated operations while preserving data privacy by keeping raw data localized and eliminating the need for centralized data aggregation
Data Source
AI summary
A method and apparatus for supporting training of an AI model at the nodes of a network is provided. The network includes control plane configured to maintain up-to-date current state information for the network, including for each node in the network. The network includes an AI model steering apparatus coupled to the control plane and configured to determine, based on an indication of the AI model and the current state information or portions thereof relevant for training the AI model, and, if obtained, the training parameters, at least one node for training of the AI model using resources and training data available to the node. The AI model steering apparatus may determine a knowledge network topology including a group of candidate nodes for training the AI model and determine a sequence of nodes from the group to form a route for training the AI model.