Non-Uniform Quantization for Federated Learning Edge Nodes
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Solution Overview
Problem
Federated learning systems face challenges with high communication costs and inefficiencies due to the need for frequent parameter updates between edge nodes and a server, especially in vehicular technologies where data heterogeneity and resource disparities among edge nodes lead to straggling nodes and increased training time.
Innovation Solution
Implementing non-uniform quantization of parameters and gradients in a machine learning model during training, which compresses data transmission and adapts to local infrastructure resources, reducing computing and communication burdens while enhancing model accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning transmits model parameters between server and edge nodes, then distributed model training is enabled with data privacy protection, but communication cost becomes very high
Solution Approach 1:
The patent extracts only the essential gradient information needed for model training from the full model parameters, transmitting only these extracted components between server and edge nodes. This reduces communication overhead while maintaining the federated learning functionality and data privacy protection.
Solution Approach 2:
The patent applies quantization to transform model parameters and gradients from high-precision floating-point representations to lower-precision discrete values. This parameter transformation significantly reduces the communication bandwidth required while preserving the essential training information, directly addressing the high communication cost problem.
2Measurement precision
If multiple iterations are performed between edge nodes and server, then model accuracy improves, but total parameter uploading and downloading increases
Solution Approach 1:
Instead of transmitting complete model parameters in each iteration, the patent extracts and transmits only the gradient differences or updates required for that specific iteration. This extraction approach maintains model accuracy improvement across iterations while significantly reducing the total volume of data transmitted over multiple rounds.
Solution Approach 2:
The patent transmits partial information (only the necessary gradient updates) rather than complete model parameters in each iteration. This partial transmission approach is sufficient for achieving model convergence and accuracy improvement while avoiding the excessive communication overhead of full parameter synchronization.
3Adaptability or versatility
If edge nodes with less computation power train models locally, then distributed learning is enabled, but training time increases due to stragglers
Solution Approach 1:
The patent applies quantization to reduce the computational complexity of model training at edge nodes. By transforming parameters to lower precision representations, edge devices with limited computation power can perform training operations more efficiently, reducing the training time for straggler nodes while maintaining distributed learning capability.
Solution Approach 2:
The patent uses simplified, lower-precision quantized representations of model parameters that require less computational resources to process. These simplified representations enable faster local training at resource-constrained edge nodes, allowing the system to tolerate heterogeneous computation powers without significant performance degradation.
4Loss of energy
If non-uniform quantization is applied to parameters and gradients, then communication cost reduces, but quantization error may increase
Solution Approach 1:
The patent applies non-uniform quantization where different quantization levels and precision are assigned to different parts of the model parameters and gradients based on their importance and distribution characteristics. This local differentiation approach reduces overall communication cost while maintaining measurement precision for critical parameters that require higher accuracy.
Solution Approach 2:
The patent transforms parameters and gradients from uniform high-precision representations to non-uniform quantized representations with varying precision levels. This parameter transformation adapts the quantization strategy to the actual data distribution, reducing communication overhead while controlling quantization errors through intelligent precision allocation.
Data Source
AI summary
A method for training a machine learning model in an edge node of a federated learning system is provided. The method includes inputting a data point into the machine learning model including parameters quantized based on a first quantization level to obtain an output, quantizing the output based on the first quantization level and a non-uniform quantization scheme, computing gradients with respect to parameters from a last layer to a first layer of the machine learning model based on the quantized output, quantizing the gradients based on a second quantization level and the non-uniform quantization scheme, and updating the machine learning model using the quantized gradients.