Federated Training Client Selection With Dynamic Participation Control
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Solution Overview
Problem
Current decentralised machine learning frameworks face challenges such as fixed client device selection, random participation, and unsuitable device selection for training, leading to inefficiencies in computational resource and transmission capacity usage.
Innovation Solution
A method for dynamically adjusting the number of client devices used for training based on evaluation metrics, ensuring optimal performance by increasing or decreasing the number of devices as needed, and selecting the most suitable devices for training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a fixed fraction of client devices is used for training, then the training process is simple to manage, but the model performance may be inadequate when the number of devices is insufficient
Solution Approach 1:
The patent implements dynamic adjustment of the number of client devices participating in training rounds. The server monitors model performance metrics and automatically increases or decreases the number of selected client devices based on whether performance thresholds are met, transforming the static fixed-fraction approach into a dynamic adaptive system that balances operational simplicity with performance reliability
2Reliability
If more client devices are used for training, then model performance improves, but computational resources and transmission capacity are wasted
Solution Approach 1:
The patent implements a feedback mechanism where the server evaluates model performance after each training round and uses this feedback to adjust the number of client devices for subsequent rounds. When performance exceeds the threshold, the server reduces the number of participating devices, thereby eliminating computational resource waste and transmission capacity overflow while maintaining adequate model performance
Solution Approach 2:
The patent dynamically changes the parameter of client device quantity based on performance evaluation. By adjusting this parameter upward when performance is insufficient and downward when performance exceeds requirements, the system optimizes the balance between model performance adequacy and computational resource efficiency
3Ease of operation
If random client devices are selected for training, then device selection is simple, but unsuitable devices may be chosen reducing training effectiveness
Solution Approach 1:
The patent enables client devices to self-evaluate their suitability for training by reporting their current state (battery level, network connection, processing capacity) to the server. The server then makes informed selection decisions based on this self-reported information, combining device autonomy with centralized optimization to improve training effectiveness while maintaining selection simplicity
Data Source
AI summary
A method for dynamically configuring a network is proposed for training a machine learning model. The network includes a server computing device and a plurality of client computing devices. The method is performed at a computing device communicatively coupled to the network and includes the following: selecting client computing devices to participate in training the model; determining a first value of an evaluation metric of the model based on the selected client computing devices; determining the presence of an adjustment trigger; adjusting the number of client computing devices used to determine the value of the evaluation metric in response to the adjustment trigger; determining a second value of the evaluation metric based on the adjusted number of client computing devices; and setting the number of client computing devices participating in training the model accordingly.