Multi-User ML Training With Zeroth-Order Updates and Low Feedback
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Solution Overview
Problem
End-to-end learning in multi-user wireless communication systems faces significant challenges due to high communication overhead required for jointly updating ML models across multiple users, particularly with increasing numbers of users and larger model sizes, which conventional techniques like back-propagation exacerbate.
Innovation Solution
Implement zeroth-order stochastic gradient descent (ZO SGD) in multi-user systems to reduce communication overhead by approximating gradients through forward passes without back-propagation, using random vectors and local updates at both base stations and user equipment, and incorporating random seeds for efficient model parameter updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional back-propagation techniques are used for end-to-end learning in multi-user systems, then model training accuracy is improved, but communication overhead increases significantly
Solution Approach 1:
The patent segments the gradient computation process by introducing intermediate representations that are exchanged between transmitter and receiver, dividing the monolithic back-propagation into manageable communication steps that reduce overall overhead
Solution Approach 2:
The patent extracts and transmits only the essential gradient information through intermediate representations, removing redundant data transmission while preserving the core learning signal needed for model updates
2Productivity
If the number of users or model size increases, then system capacity and model performance are improved, but communication overhead increases exponentially
Solution Approach 1:
The patent implements partial action by transmitting intermediate representations at selective stages during training, performing only the necessary gradient information exchange rather than continuous full-model updates, thereby reducing communication overhead while maintaining learning effectiveness
Solution Approach 2:
The patent segments the model parameters and gradient computations into manageable intermediate representations that can be transmitted efficiently, breaking down the exponential communication burden into linear or polynomial scaling components
3Reliability
If frequent model updates are performed across multiple users, then learning convergence is improved, but feedback overhead and memory requirements increase
Solution Approach 1:
The patent extracts only the critical gradient information needed for convergence and transmits it through compact intermediate representations, removing unnecessary feedback data while preserving the essential learning signal
Solution Approach 2:
The patent performs partial model updates by exchanging intermediate representations at strategic training intervals rather than after every parameter update, achieving sufficient convergence while dramatically reducing feedback overhead and memory requirements
Data Source
AI summary
Various aspects of the present disclosure relate to storing a ML model and a set of ML parameters associated with the ML model. Aspects of the present disclosure relate to generating a first and second sets of forward-pass values based on perturbations of the set of ML parameters by a random vector in a positive direction and a negative direction, respectively. Aspects of the present disclosure relate to transmitting, to a set of UEs, a set of training messages containing the first and second sets of forward-pass values, and receiving a set of loss difference values, each associated with a UE of the set of UEs. Aspects of the present disclosure relate to determining a model update decision based on the set of loss difference values and transmitting, to the set of UEs, an update message indicating the model update decision.