Compressed Gradient Signaling for Wireless Federated Learning
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Solution Overview
Problem
In wireless communications systems, the efficient communication of high-dimensional machine learning model updates, such as those used in federated learning, poses challenges due to the large amount of data required, leading to resource consumption and potential privacy issues, especially when using edge devices with complex models like ResNet.
Innovation Solution
The implementation of a multi-stage compression technique for signaling gradient vectors, where user equipment (UE) receives configuration information from a base station to generate and transmit compressed gradient vectors using partitioning parameters, quantization codebooks, and bit allocation schemes, allowing for efficient communication of local stochastic gradients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If high-dimensional machine learning model updates are communicated using edge devices, then federated learning can be implemented, but wireless resource consumption increases and privacy issues arise
Solution Approach 1:
The patent extracts only the essential gradient information from the complete model updates and transmits only this compressed representation to the base station. By taking out and transmitting only the necessary gradient vectors rather than full model parameters, the system enables federated learning while significantly reducing wireless resource consumption and protecting user privacy.
Solution Approach 2:
The patent applies quantization to change the precision parameters of gradient vectors, converting high-precision floating-point gradient information into lower-precision representations. This parameter change reduces the amount of data that needs to be transmitted over the wireless channel, thereby reducing resource consumption while maintaining the essential learning functionality.
2Measurement precision
If complete gradient vectors are transmitted from UEs to base station, then model update accuracy is maintained, but communication overhead increases
Solution Approach 1:
The system extracts and transmits only the gradient vector information that is essential for model updates, rather than transmitting complete model parameters or redundant data. This extraction approach maintains the necessary accuracy for model convergence while significantly reducing the quantity of data transmitted over the wireless channel.
Solution Approach 2:
The patent creates compressed representations (copies) of the gradient vectors through quantization and compression techniques. These compressed copies retain the essential information needed for accurate model updates while occupying less communication bandwidth, thus reducing overhead while preserving accuracy.
3Loss of energy
If compression techniques are applied to gradient vectors, then resource usage is reduced, but quantization error may affect learning accuracy
Solution Approach 1:
The patent carefully controls the quantization parameters to balance compression and accuracy. By adjusting the quantization bit-depth and compression level according to the specific learning task requirements, the system achieves sufficient resource reduction while maintaining learning accuracy within acceptable bounds. The base station can request different compression levels based on observed model convergence behavior.
Data Source
AI summary
Methods, systems, and devices for wireless communications are described that support signaling of compressed gradient vectors in a machine learning system that utilizes federated learning. The compressed gradient vectors may be used to report stochastic gradients from multiple edge devices (e.g., multiple user equipment (UE) devices) that are combined into a global model at an edge server (e.g., a base station). A base station may configure a UE with one or more parameters for quantizing a local stochastic gradient, and for reporting the quantized local stochastic gradient in a set of compressed gradient vectors. Each vector of the compressed gradient vectors may be associated with a different stage of a multi-stage compression procedure for reporting the local stochastic gradient, and multiple reports from multiple UEs may be aggregated in a federated learning procedure associated with a machine learning algorithm.