NWDAF Federated Learning Coordination for Timely Model Training
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Solution Overview
Problem
In federated learning within a network data analytics function (NWDAF) service, there is a challenge in completing training operations within the requested time and monitoring abnormal events across multiple local NWDAFs to ensure stable analytics service provision.
Innovation Solution
A central NWDAF manages federated learning by determining local NWDAFs, transmitting behavior instructions, receiving local models, generating a global model, and monitoring for abnormal events to ensure timely and accurate service delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning is performed across multiple local NWDAFs, then learning accuracy and model robustness are improved, but training completion time and system coordination complexity increase
Solution Approach 1:
The central NWDAF determines the federated learning configuration and selects participating local NWDAFs before training begins. This preliminary setup includes defining the learning task, setting convergence criteria, and establishing communication protocols, which streamlines the subsequent distributed training process and reduces overall completion time.
Solution Approach 2:
Local NWDAFs continuously report training progress, loss metrics, and convergence status to the central NWDAF. The central NWDAF uses this feedback to monitor training completion and dynamically adjust coordination strategies, ensuring timely termination when convergence criteria are met while maintaining learning accuracy.
2Reliability
If federated learning is performed across multiple local NWDAFs, then learning accuracy and model robustness are improved, but system coordination complexity increases
Solution Approach 1:
The system is segmented into a central NWDAF that handles high-level coordination and local NWDAFs that execute training independently. This segmentation distributes complexity across multiple components, with each entity having a specialized function, thereby managing overall system coordination complexity while enabling accurate federated learning.
Solution Approach 2:
The central NWDAF acts as an intermediary that mediates between multiple local NWDAFs. It receives analytics requests, determines federated learning configuration, coordinates training participation, and aggregates results. This intermediary role simplifies the interaction model between distributed entities, reducing coordination complexity while maintaining learning accuracy.
3Reliability
If abnormal event monitoring is implemented across local NWDAFs, then service stability is improved, but information processing overhead increases
Solution Approach 1:
Local NWDAFs monitor for abnormal events during training and provide feedback to the central NWDAF. The central NWDAF processes this information to determine appropriate responses, such as adjusting training parameters or excluding problematic participants. This targeted feedback mechanism improves service stability by quickly responding to abnormalities while minimizing unnecessary information processing overhead through selective monitoring and reporting.
Data Source
AI summary
The present disclosure relates to an operation method of a central NWDAF comprising the steps of: receiving an analytics request message from a network function; determining, on the basis of the analytics request message, a local NWDAF included in a federated learning (FL) group; transmitting an FL local behavior instruction to the local NWDAF; receiving a response message from the local NWDAF in response to the FL local behavior instruction; receiving, on the basis of the FL local behavior instruction, a local model generated by means of the local NWDAF from the local NWDAF; generating a global model on the basis of the local model; and transmitting the global model to the NWDAF.