Model Training Across Wireless Nodes with Partial Parameter Exchange
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Solution Overview
Problem
The challenge of training AI models in a wireless network environment is the high data transmission pressure and inefficiency due to heterogeneous network nodes, leading to slow training progress and data privacy concerns.
Innovation Solution
A distributed model training method where nodes in a set update and send partial or complete model parameters to a next-hop node, reducing data transmission and adapting to node heterogeneity for improved training efficiency and privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning algorithm is used to train models distributedly, then data privacy is protected, but data transmission pressure increases significantly
Solution Approach 1:
The patent segments the model training process into multiple stages, where only partial model parameters (gradients, updates) are transmitted between nodes and the central server, rather than transmitting complete models or raw data. This segmentation reduces the quantity of transmitted data while maintaining privacy protection.
Solution Approach 2:
The patent extracts and transmits only the essential model parameters (gradients and updates) needed for training, removing unnecessary data components. This extraction approach minimizes transmission pressure while preserving the core functionality of federated learning for privacy protection.
2Measurement precision
If complete model parameters are transmitted between nodes, then training accuracy is maintained, but transmission overhead increases
Solution Approach 1:
The patent applies partial action by transmitting only the necessary portion of model parameters (gradients and updates) rather than complete models. This partial transmission maintains training accuracy while significantly reducing transmission overhead, as only the changes needed for learning are communicated.
3Adaptability or versatility
If nodes with poor performance are included in training, then network heterogeneity is addressed, but overall training speed decreases
Solution Approach 1:
The patent implements dynamic adaptation where the system adjusts to nodes with varying performance characteristics. The training process is designed to be flexible, allowing nodes with different computing capabilities to participate without requiring uniform performance, thus maintaining heterogeneity adaptation while managing training speed through asynchronous or adaptive mechanisms.
4Stability of the object's composition
If a central server aggregates all model data, then model convergence is achieved, but management complexity increases
Solution Approach 1:
The patent segments the centralized aggregation process into distributed operations where nodes perform local updates and only exchange essential parameters. This segmentation reduces the management burden on the central server while maintaining model convergence through coordinated parameter updates across the network.
Data Source
AI summary
A model training method and an apparatus relate to the field of communication technologies. This can reduce data transmission pressure and improve a training speed and training efficiency when a model is trained via each network node. The method includes: a first node updates an obtained first model to obtain an updated first model, and sends the updated first model to a next-hop node. The first node is any node in a node set, and the node set is used to train the first model. The updated first model converges on the first node. The next-hop node is a node in the node set.