NRF Authorization Mechanism for 5G Federated Learning Roles
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Solution Overview
Problem
Current 5G networks lack an authorization mechanism to ensure that network data analytics functions (NWDAFs) are authorized to operate as federated learning (FL) servers and clients, particularly in roaming scenarios, and existing OAuth 2.0 frameworks are insufficient for determining which NWDAFs can work as FL servers and clients, posing security risks and user consent issues.
Innovation Solution
An authorization mechanism is implemented using a network repository function (NRF) to manage FL profile data, including role parameters, access tokens, and user consent, ensuring authorized data sharing and secure FL operations across different public land mobile networks (PLMNs).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning is implemented across multiple decentralized edge devices or servers, then data privacy and security are improved, but authorization and access control mechanisms become more complex
Solution Approach 1:
The patent introduces an authorization server as an intermediary component that mediates between FL servers and FL clients. This server manages access tokens and authorization credentials, providing a centralized authorization mechanism that simplifies the complex interactions between decentralized FL participants while maintaining data privacy. The intermediary handles authentication and authorization logic, reducing the burden on individual FL nodes.
2Productivity
If traditional centralized machine learning techniques are used where all local datasets are uploaded to one server, then model training efficiency is improved, but data security and privacy protection deteriorate
Solution Approach 1:
The patent segments the centralized learning process into distributed components across multiple FL servers and clients. Instead of uploading all data to a single server, the system divides the learning task into segments that can be executed locally on decentralized nodes. Each participant contributes to model training using their local data without exposing the raw data, thus maintaining both training efficiency and data security through segmented, distributed computation.
3Reliability
If decentralized federated learning approaches are used, then data privacy is improved, but the assumption of identically distributed local data samples becomes more difficult to satisfy
Solution Approach 1:
The patent implements dynamic adaptation mechanisms that allow the federated learning system to handle non-identically distributed data across different nodes. The system can dynamically adjust learning parameters, aggregation weights, and communication strategies based on the actual data distribution characteristics of each participant. This dynamic approach enables the system to maintain effectiveness despite heterogeneous data distributions while preserving the privacy benefits of decentralization.
Data Source
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AI summary
Various embodiments relate to network repository function apparatus configured to implement an authorization mechanism for a federated learning (FL) training process, including: at least one processor; and at least one memory storing instructions, that when executed by the at least one processor, cause the apparatus at least to: receive from a network data analytics function (NWDAF) NWDAF FL profile data including a FL process role parameter; receive an access token from the FL server for the NWDAF that is the potential FL client; determine if the FL access token request is authorized for the NWDAF based upon the FL profile data; and send an access token for the NWDAF to the FL server when access token request for the NWDAF is authorized.