UE Data Distribution Adjustment for Federated Learning Bias
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Solution Overview
Problem
Federated learning models trained on biased data distributions suffer from overfitting and reduced accuracy in classifying variations due to skewed input and output distributions, leading to inaccurate gradient aggregation and model performance.
Innovation Solution
Adjust local data distributions at user equipment (UEs) to match target conditions, such as class instance counts and ratios, by receiving updates from a network node, and training models with unbiased data distributions to improve model accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If federated learning models are trained on local data instances with biased distributions, then training can proceed independently at each UE, but the model accuracy and generalization performance deteriorate due to overfitting and skewed distributions
Solution Approach 1:
The patent changes the distribution parameters of local data instances by receiving update indications from the network node that specify target distributions. Each UE adjusts its local data instance distribution to match the target distribution, thereby transforming the biased local distributions into unbiased ones that align with the global data distribution, resolving the contradiction between independent training and model accuracy
Solution Approach 2:
The network node provides feedback to each UE about the desired data distribution characteristics through update indications. UEs use this feedback to adjust their local data instance distributions, creating a closed-loop system where distribution bias is continuously corrected, enabling both independent training and high model accuracy
2Manufacturing precision
If local data distributions are adjusted to match target conditions, then model accuracy and generalization improve, but communication overhead and coordination complexity increase between UEs and network node
Solution Approach 1:
The network node acts as an intermediary that centralizes the complex task of distribution analysis and update calculation. Instead of UEs directly coordinating with each other, the network node receives distribution information from UEs, computes the necessary updates, and provides guidance back to UEs, simplifying the overall system complexity while achieving distribution alignment
Solution Approach 2:
The patent implements partial action by having UEs report only essential distribution characteristics (such as class instance counts or distribution statistics) rather than complete data instances. This partial information exchange is sufficient for the network node to compute distribution updates, reducing communication overhead while still enabling effective distribution alignment
3Manufacturing precision
If data distribution updates are transmitted from network node to UEs, then biased distributions are corrected, but communication bandwidth and transmission time are consumed
Solution Approach 1:
The patent uses lightweight, compact representations of distribution updates instead of transmitting large volumes of actual data instances. The update indications contain only essential distribution parameters or correction vectors, which are small in size and can be quickly transmitted, providing an efficient, low-overhead mechanism for distribution correction that minimizes communication time
Data Source
AI summary
A method for wireless communication at a first user equipment (UE) includes transmitting, to a network node, a first message indicating one or more distributions of a group of local data instances associated with a machine learning model at the first UE, each local data instance of the group of local data instances associated with a class of a group of classes. The method also includes receiving, associated with transmitting the first message, from the network node, a second message indicating an update to the group of local data instances, for satisfying one or more data distribution conditions. The method further includes training, associated with the update to the group of local data instances, the machine learning model.