A federated user prediction method for 6G edge network

By employing a federated learning-based user prediction method in 6G edge networks, utilizing patch feature extraction and Transformer Encoder combined with KD-trees, the accuracy and privacy issues of traditional prediction mechanisms are resolved, achieving efficient and secure user prediction and resource optimization, suitable for diverse 6G application scenarios.

CN122372445APending Publication Date: 2026-07-10XIDIAN UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
XIDIAN UNIV
Filing Date
2026-05-26
Publication Date
2026-07-10

AI Technical Summary

Technical Problem

Traditional user prediction mechanisms in 6G edge networks suffer from limited prediction accuracy and insufficient generalization performance, making them unsuitable for diverse mobile scenarios. Furthermore, centralized training leads to risks of data privacy leaks and network congestion, failing to meet the needs of real-time connection and resource scheduling for massive numbers of devices.

Method used

A user prediction method based on federated learning is adopted. The user prediction model is constructed by dividing the model into a patch feature extraction module and a prediction result alignment module. Combined with Transformer Encoder and KD tree, the model can efficiently capture user behavior features. The model is also trained collaboratively between the edge core network and the central core network to avoid privacy leakage during data transmission.

Benefits of technology

It achieves efficient and accurate user prediction, reduces computational complexity and latency, optimizes resource allocation, ensures data privacy and security, adapts to multiple business scenarios, and supports 6G applications such as intelligent transportation and industrial IoT.

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Abstract

This invention discloses a federated user prediction method for 6G edge networks, comprising the following steps: Step 1: Preprocessing the input user equipment movement trajectory information; Step 2: Constructing a patch feature extraction module and optimizing it independently for each channel; Step 3: Constructing a prediction result alignment module, using the output of the patch feature extraction module as the input; Step 4: Constructing a user prediction model using the patch feature extraction module and the prediction result alignment module, and defining the loss function of the 6G user prediction model; Step 5: Constructing a 6G edge network user prediction architecture based on federated learning; Step 6: Training the 6G edge network user prediction architecture based on federated learning; Step 7: Performing 6G user prediction using the trained 6G edge network user prediction architecture based on federated learning. This invention features accurate and efficient prediction, optimized resource allocation, data privacy and security, and deep integration with the network architecture.
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