Collaborative ML Training Mode Selection for Energy-Constrained Devices
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Solution Overview
Problem
Existing wireless communication networks face challenges in reducing energy consumption during collaborative training of machine learning models, particularly for devices with limited energy budgets, due to varying radio channel conditions and high communication overhead.
Innovation Solution
A method involving a network node training a first machine learning model to select optimal training modes for a second model based on radio channel state information and energy consumption estimates, enabling iterative training with modes like split learning, federated learning, or idle modes, using multi-agent reinforcement learning to minimize energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If devices participate in collaborative training of machine learning models, then model accuracy is improved, but energy consumption increases
Solution Approach 1:
The patent implements dynamic training mode selection that adapts to varying radio channel conditions. Devices can switch between split learning mode, federated learning mode, and idle mode based on real-time channel state information. This dynamic adaptation allows the system to maximize participation and accuracy when channel conditions are good, while reducing energy consumption when conditions deteriorate, thus resolving the contradiction between accuracy improvement and energy consumption.
2Productivity
If more devices participate in collaborative training, then training efficiency is improved, but communication overhead increases
Solution Approach 1:
The patent segments the machine learning model training process into multiple modes: split learning mode where the model is divided between device and network, federated learning mode where only model updates are exchanged, and idle mode. This segmentation allows the system to optimize the balance between participation and communication overhead by selecting appropriate modes for different devices based on channel conditions, thereby improving training efficiency while controlling communication overhead.
3Adaptability or versatility
If devices with limited energy budgets participate in training, then network collaboration is enhanced, but energy depletion occurs
Solution Approach 1:
The patent changes the operational parameters of participating devices by introducing energy consumption estimates as a key decision factor. The system evaluates energy budgets and adjusts training participation accordingly, allowing devices with limited energy to participate in lower-intensity modes (such as federated learning or idle mode) while maintaining network collaboration. This parameter-based adaptation enhances versatility of network participation while preventing energy depletion.
Data Source
AI summary
A method may comprise: training, by a network node, a first machine learning (ML) model for selection of training modes for collaborative training of a second ML model by a plurality of devices, wherein the first ML model is configured for selection of the training modes based on radio channel state information (CSI) of the devices and an estimate of energy consumption for training the second ML model by a respective device of the devices; transmitting the first ML model to the devices; transmitting the second ML model to the devices; receiving radio CSI from each of the devices; sharing the received radio CSI with the devices; receiving, from the devices, indications of the training modes of the devices for the collaborative training of the second ML model; and performing iterative training of the second ML model.