Energy-Aware Mode Selection for Collaborative ML Training
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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, using multi-agent reinforcement learning to minimize energy consumption by devices, and performing iterative training with split learning, federated learning, or idle modes.
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 where devices can switch between split learning mode and federated learning mode based on real-time radio channel conditions and energy status. This dynamic adaptation allows the system to optimize the balance between training accuracy and energy consumption, resolving the contradiction by making the training approach flexible rather than fixed.
Solution Approach 2:
The system changes key parameters including the cut-layer position in split learning, training mode selection, and participation decisions based on channel state information and energy consumption estimates. By adjusting these parameters dynamically, the system achieves both improved accuracy and reduced energy consumption compared to static approaches.
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 into multiple layers and assigns different cut-layer positions to different devices. This segmentation allows training to be distributed across multiple devices simultaneously, improving training efficiency while managing communication overhead by dividing the computational and communication burden among participants.
Solution Approach 2:
The network node acts as an intermediary that coordinates training modes, manages cut-layer assignments, and aggregates results from multiple devices. This intermediary function enables efficient coordination of multiple participating devices while minimizing redundant communication and optimizing the overall training process.
3Use of energy by moving object
If split learning mode is used with early cut-layers, then energy consumption is reduced, but training accuracy deteriorates
Solution Approach 1:
The system dynamically adjusts the cut-layer position based on device-specific conditions such as energy status and channel quality. Devices with sufficient energy and good channel conditions can use later cut-layers for higher accuracy, while energy-constrained devices use earlier cut-layers. This dynamic adjustment resolves the contradiction by matching cut-layer depth to device capabilities.
Solution Approach 2:
Different devices are assigned different cut-layer positions tailored to their local conditions (energy budget, channel quality, computational capability). This local optimization allows each device to operate at its optimal point, with the collective result achieving both energy efficiency and training accuracy across the distributed system.
Data Source
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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.