Model Training Batching for Network-Limited Federated Learning
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Solution Overview
Problem
Federated learning is hindered by long communication delays due to limited network capabilities, leading to inefficiencies in model training when large amounts of data need to be exchanged between distributed devices.
Innovation Solution
Divide training data into groups based on communication capability, processing and transmitting these groups in batches to reduce idle waiting times and improve overall training efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is exchanged between distributed devices via network in federated learning, then model training can be performed collaboratively, but communication duration becomes greater than training duration due to limited network capability
Solution Approach 1:
The patent segments the training data into multiple groups and processes them in batches. Each batch processes a subset of data groups, allowing the system to transmit smaller amounts of data more frequently. This segmentation of data processing into manageable batches reduces the communication burden per transmission cycle while maintaining collaborative training across distributed devices.
Solution Approach 2:
The patent performs preliminary processing of data groups into batches before transmission. By pre-organizing and preparing data in batch format locally at each device, the system minimizes the amount of data that needs to be transmitted during the actual training process. This preliminary organization of data reduces communication overhead and allows training to proceed more efficiently.
2Measurement precision
If large amounts of data are transmitted between devices, then model training accuracy can be improved, but idle waiting duration increases due to communication delays
Solution Approach 1:
The patent implements periodic action by transmitting data in repeated batch cycles. Instead of transmitting all data at once or waiting for complete data sets, the system periodically transmits batches of processed data groups in iterative cycles. This periodic batch transmission keeps devices continuously engaged in processing and transmitting smaller data portions, eliminating idle waiting periods while maintaining the accuracy benefits of comprehensive data processing over multiple iterations.
Data Source
AI summary
A model training method is provided and applied to the field of artificial intelligence technologies. In the method, a plurality of pieces of data in a training process are divided into a plurality of groups of data, and a data amount of each group of data is related to a communication capability of a training apparatus. The training apparatus processes the plurality of groups of data in batches based on a model, and transmits a plurality of parts of data obtained by processing the plurality of groups of data to another training apparatus in batches, to train the model.