5G Core NWDAF Selection for Federated Learning Coordination
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Solution Overview
Problem
Current 3GPP specifications do not adequately address the challenges of NWDAF registration, discovery, and selection in Federated Learning (FL) within the 5G core network, including the coordination of multiple Network Data Analytics Functions (NWDAFs) for efficient model training while ensuring data privacy and security.
Innovation Solution
Proposed mechanisms for NWDAF registration, discovery, and selection in the FL preparation phase, considering capability and availability, and monitoring and re-selection in the execution phase, to manage FL operations effectively in the 5G core network, including procedures for NWDAF profile registration, discovery, and selection, as well as monitoring and re-selection of Client NWDAFs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple NWDAFs are coordinated for federated learning model training, then model training efficiency and data privacy are improved, but system complexity and coordination overhead increase
Solution Approach 1:
The system segments NWDAF functions into server NWDAF and client NWDAF roles, with each having specific responsibilities. Server NWDAF coordinates the federated learning process while client NWDAFs perform local model training, dividing the complex task into manageable segments that improve privacy while controlling system complexity
Solution Approach 2:
The server NWDAF acts as an intermediary that coordinates between multiple client NWDAFs without accessing their raw data. It manages the federated learning process, aggregates model updates, and maintains privacy by never directly handling sensitive data, thus improving data privacy while managing system complexity through centralized coordination
2Ease of operation
If NWDAF registration and discovery mechanisms are implemented, then FL operation management is improved, but signaling overhead and network traffic increase
Solution Approach 1:
NWDAF instances perform preliminary registration with the network repository function before participating in federated learning. This pre-registration establishes their capabilities and availability in advance, enabling efficient discovery and selection without requiring extensive real-time signaling during FL operations
Solution Approach 2:
The system implements feedback mechanisms where client NWDAFs report their status, capability, and availability to the server NWDAF. This feedback enables intelligent selection of appropriate client NWDAFs for federated learning tasks, improving operation management while optimizing signaling by only communicating necessary information
3Productivity
If client NWDAF selection is optimized based on capability and availability, then model training efficiency is improved, but selection process complexity increases
Solution Approach 1:
Client NWDAFs self-report their capabilities, availability status, and computational resources to the network repository function and server NWDAF. This self-service approach eliminates the need for complex centralized assessment mechanisms, improving model training efficiency by enabling quick selection while keeping the selection process simple through standardized capability declarations
Data Source
AI summary
Systems and methods are disclosed that related to Distributed Machine Learning (DML) or Federated Learning (FL) in core network of a mobile or cellular communications system. In one embodiment, a method performed by a server Network Data Analytics Function (NWDAF) for selecting one or more client NWDAFs comprises transmitting, to each of a set of client NWDAFs, a preparation request for DML or FL and receiving, from each of at least some of the set of client NWDAFs, a response to the preparation request for DML or FL. The method further comprises selecting one or more client NWDAFs based on the responses to the preparation requests for DML or FL. In this manner, the network is enabled to support DML or FL operations.