Federated Model Updates with Consistency-Driven Training Parameters
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Solution Overview
Problem
In federated learning, the fixed or ascending/descending training information settings can lead to suboptimal model updates, affecting the accuracy of model training as clients may miss the optimal upload occasions.
Innovation Solution
A method for implementing model update that involves calculating first training information based on difference consistency information from multiple clients, using formulas to determine optimal training batch quantities and learning rates, and applying an exponential moving average to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fixed or ascending/descending training information settings are used, then the service device can simplify the training process, but the client may miss optimal upload occasions and model training accuracy deteriorates
Solution Approach 1:
The patent transforms the static, fixed training information settings into dynamic settings by calculating training information (number of training batches and learning rate) based on real-time difference consistency information from multiple clients. This allows the training parameters to adapt automatically to the current model state and client performance, resolving the contradiction between operational simplicity and training accuracy.
Solution Approach 2:
The patent implements a feedback mechanism where the service device receives difference information from clients, calculates difference consistency information, and uses this feedback to dynamically adjust training information for the next round. This closed-loop feedback system ensures that training parameters are continuously optimized based on actual training outcomes, improving model training accuracy while maintaining automated operation.
2Measurement precision
If the service device calculates training information based on difference consistency information from multiple clients, then model training accuracy is improved, but the calculation complexity and system overhead increase
Solution Approach 1:
The patent enables the system to self-regulate by automatically calculating training information based on difference consistency information without requiring manual intervention or complex external control systems. The service device autonomously processes difference information from clients, computes consistency metrics, and generates appropriate training parameters, reducing the need for complex external management while improving training accuracy.
Solution Approach 2:
The patent focuses calculation efforts on computing specific key parameters (difference consistency information, number of training batches, learning rate) rather than analyzing entire model structures. By changing and optimizing only the necessary parameters based on difference information, the system achieves improved training accuracy with controlled computational overhead.
3Adaptability or versatility
If training information is set in ascending or descending manner only, then the service device can maintain simple control logic, but the client cannot adapt to optimal upload occasions and training effectiveness decreases
Solution Approach 1:
The patent performs preliminary calculation of training information (number of training batches and learning rate) based on difference consistency information before the client executes the next training round. This preliminary action ensures that clients receive optimized training parameters in advance, enabling them to adapt to optimal upload occasions and improving overall training efficiency without requiring complex real-time adjustments during training execution.
Data Source
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AI summary
A method for implementing model update is provided and used in a federated update framework. The method includes: A service device receives difference information uploaded by at least two clients. The service device performs calculation based on the difference information uploaded by the at least two clients, to obtain first difference consistency information, where the first difference consistency information indicates a consistency degree of the difference information uploaded by the at least two clients. The service device performs calculation based on the first difference consistency information, to obtain first training information, where the first training information is used to train a third model, and the third model is obtained by updating the first model by the service device based on the difference information uploaded by the at least two clients. The service device sends the first training information to the at least two clients. This method can improve model accuracy.