Federated Learning Resource Scheduling for Heterogeneous 5G Terminals
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Solution Overview
Problem
Existing federated learning methods lack a feasible solution for scheduling training parameters of terminal devices in wireless networks, particularly in 5G networks, due to varying data processing and transmission capabilities and environmental changes, leading to inefficiencies and time delays.
Innovation Solution
A resource scheduling method where a network device generates and sends configuration information to terminal devices, indicating characteristics of sample data and model training, tailored to their processing and transmission capabilities, ensuring efficient model training in wireless environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If federated learning is implemented in wireless networks without resource scheduling, then terminal devices can participate in model training, but time delays and inefficiencies occur due to varying data processing and transmission capabilities
Solution Approach 1:
The patent implements dynamic resource scheduling that adapts to varying terminal device capabilities and network conditions. The network device adjusts configuration information dynamically based on real-time data processing capabilities and transmission rates of terminal devices, enabling the system to optimize federated learning efficiency while minimizing time delays through continuous adaptation rather than static allocation.
Solution Approach 2:
The patent changes key parameters including data processing capability thresholds, transmission rate requirements, and configuration information content based on terminal device performance. By adjusting these parameters according to device capabilities, the system resolves the contradiction between including more devices (improving productivity) and managing time delays from heterogeneous device performance.
2Productivity
If configuration information is customized for each terminal device based on capabilities, then resource allocation efficiency improves, but system complexity increases
Solution Approach 1:
The patent applies local quality by providing customized configuration information to each terminal device based on its specific capabilities. Each device receives tailored parameters such as data processing requirements and transmission configurations matched to its performance characteristics, improving resource allocation efficiency while the network device manages the complexity centrally rather than distributing it across all devices.
Solution Approach 2:
The network device serves as an intermediary that centralizes the complexity of capability assessment and configuration generation. It collects capability information from terminal devices, processes this data to determine optimal configuration parameters, and distributes customized configurations back to devices. This intermediary approach improves resource allocation while containing system complexity in a centralized management function.
Data Source
AI summary
A resource scheduling method, an apparatus and a readable storage medium. In a process in which a terminal device participates in federated learning, a network device first generates first information, the first information including configuration information, and specifically, the configuration information is used to indicate characteristics of sample data, and/or, characteristics of a model to be trained for the terminal device to perform model training; and then, the network device sends the first information to the terminal device, and the terminal device acquires the configuration information in the first information. In the present embodiment, the network device determines, according to a data processing capability and/or a data transmission capability of the terminal device, that the terminal device participates in a current round of federated learning, and generates and sends the configuration information of a training parameter that matches the terminal device.