Federated Parameter Training Gradient Reporting
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Solution Overview
Problem
Existing wireless communication systems, particularly in 5G NR, face challenges in efficiently updating machine learning model parameters across multiple user equipment (UEs) due to high overhead in transmitting local gradients.
Innovation Solution
A non-coherent over-the-air aggregation mechanism is introduced, allowing UEs to report gradient values using a pair of resource elements (REs) for magnitude or a single RE for sign, reducing the overhead and relaxing phase synchronization requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If UEs transmit local gradients to update machine learning model parameters, then model training accuracy is improved, but communication overhead increases
Solution Approach 1:
The patent extracts only the essential gradient information (sign and magnitude) from complete gradient vectors, transmitting only these critical components back to the network node. This selective extraction reduces the amount of data that needs to be transmitted while preserving the most important training information, thereby reducing communication overhead while maintaining model training effectiveness
Solution Approach 2:
The patent changes the representation parameters of gradient information from transmitting full gradient vectors to transmitting simplified representations (sign and magnitude in quantized form). This parameter transformation reduces the dimensionality and data volume of transmitted information while retaining the essential directional and intensity information needed for model parameter updates
2Loss of information
If traditional gradient transmission methods are used, then complete gradient information is transmitted, but phase synchronization requirements increase system complexity
Solution Approach 1:
The patent uses disposable, simple analog signals for gradient transmission that do not require complex synchronization mechanisms. Each UE transmits simple analog representations of gradient sign and magnitude that can be directly aggregated at the network node without requiring precise phase alignment, thereby eliminating the need for complex phase synchronization while preserving gradient information
Solution Approach 2:
The patent replaces the traditional digital gradient transmission mechanism (which requires precise timing and phase synchronization) with an analog over-the-air aggregation mechanism. Multiple UEs transmit analog signals simultaneously that are naturally aggregated in the air interface, eliminating the need for complex digital synchronization protocols and reducing system complexity
Data Source
AI summary
A UE receives parameters of a machine learning model from a network node; calculate a gradient value relative to a parameter of the parameters of the machine learning model, the gradient value including a positive gradient value or a negative gradient value. The UE may transmit, in one resource element (RE) of a pair of REs to the network node, an analog signal indicating a magnitude of the gradient value relative to the parameter of the parameters of the machine learning model. The UE may transmit or skip transmission of, in a single RE to the network node, an analog signal based on the gradient value, the single RE designated for indicating the positive gradient value or the negative gradient value of the gradient value relative to the parameter of the parameters of the machine learning model.