Federated Learning Timing Configuration for Wireless Terminals
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Solution Overview
Problem
The timing of transmitting locally trained machine learning models from user equipment (UE) to a central unit in federated learning systems is critical for improving terminal machine learning capabilities, but existing methods lack efficient timing mechanisms to ensure timely and effective model updates.
Innovation Solution
The proposed solution involves a method and apparatus for coordinating federated learning models between a centralized entity and UEs, using a minimization of drive tests (MDT) configuration with training timing information, including deadlines and periodic reporting, to manage the transmission of local machine learning models and ensure timely updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning is implemented without timing mechanisms, then data privacy is preserved and local computational power is utilized, but model update timeliness deteriorates and communication efficiency worsens
Solution Approach 1:
The patent applies preliminary action by configuring timing information and deadlines in advance before the federated learning training process begins. The central unit provides configuration information including time limits for model completion and transmission deadlines to user equipment, enabling UEs to plan and execute model training and transmission within predetermined time windows, thus ensuring timely model updates without real-time coordination overhead.
2Manufacturing precision
If local models are transmitted frequently to improve global model accuracy, then model performance improves, but communication load increases and energy consumption rises
Solution Approach 1:
The patent implements periodic action by establishing periodic reporting intervals for local model transmissions. The configuration information includes time limits that define periodic windows for model completion and transmission, allowing user equipment to transmit models at regular intervals rather than continuously. This periodic transmission schedule maintains model accuracy improvements while significantly reducing communication load and energy consumption compared to frequent transmissions.
3Productivity
If strict timing deadlines are enforced for model transmission, then model update efficiency improves, but system flexibility deteriorates and device adaptability decreases
Solution Approach 1:
The patent applies dynamics by making the timing configuration adaptive rather than fixed. The central unit can dynamically adjust time limits and deadlines based on network conditions, device capabilities, and training progress. The system allows flexible interpretation of timing requirements, enabling devices to adapt their training schedules while still meeting overall efficiency goals, thus balancing productivity with adaptability.
Data Source
AI summary
There are provided measures for enablement of federated machine learning for terminals to improve their machine learning capabilities. Such measures exemplarily comprise, at a terminal, receiving a configuration indicative of an instruction to participate in federated learning of a global machine learning model, the configuration including timing information related to said federated learning, and performing, based on said configuration, a machine learning process based on undertaken network performance related measurements, wherein said timing information includes a time limit with respect to a local machine learning model resulting from said machine learning process, and said time limit is a specification of a moment in time by when said local machine learning model is to be completed or a specification of a moment in time by when transmission of said local machine learning model is to be completed, and wherein said configuration is a minimization of drive tests configuration.