Federated Learning Coordination for 5G Model Training
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Collating large datasets for machine-learning model training in 5G communication networks is slow, resource-intensive, and raises security concerns, especially when dealing with sensitive or private data.
Innovation Solution
Implementing a collaborative (federated) learning process where multiple network functions train local models using machine-learning algorithms and share updates with a central aggregator, such as the NWDAF, to combine into a single model update, thereby distributing the data processing and reducing security risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is collated at a single network entity for machine-learning model training, then model training can be performed centrally, but the process becomes slow and resource-intensive
Solution Approach 1:
The patent segments the centralized model training process into distributed local training tasks across multiple network entities. Each entity trains a local model using its own data, and the coordination entity aggregates these local models to form a global model. This segmentation eliminates the need to collect all data at one location, thereby improving training speed and reducing resource overhead at any single entity.
Solution Approach 2:
The patent transitions from a single-dimension centralized training approach to a multi-dimensional distributed training architecture. By adding the dimension of spatial distribution across multiple network entities, the system achieves parallel training operations, which improves overall productivity while distributing resource consumption across multiple nodes rather than concentrating it at one location.
2Reliability
If data is collated at a single network entity, then centralized model training is enabled, but security risks increase due to handling sensitive or private data
Solution Approach 1:
The patent extracts the sensitive data from the centralized collection process and keeps it distributed at local network entities. Only model parameters and gradients (not the actual sensitive data) are transmitted to the coordination entity. This extraction approach maintains security by preventing centralized access to raw sensitive data while still enabling collaborative model training.
Solution Approach 2:
The patent introduces the coordination entity as an intermediary that facilitates model training without directly handling sensitive data. The intermediary aggregates local models and coordinates the federated learning process, acting as a mediator that enables collaboration while maintaining data privacy at source locations.
3Reliability
If data is distributed across multiple network entities, then security is improved, but coordinating training across entities increases system complexity
Solution Approach 1:
The patent implements a universal coordination entity that handles multiple functions: model distribution, training coordination, gradient aggregation, and model updating. This multi-functional coordinator simplifies the overall system architecture by providing a single point of control that manages all aspects of federated learning, reducing coordination complexity despite distributed data locations.
Solution Approach 2:
The patent manages coordination complexity by changing parameters such as aggregation weights, learning rates, and selection criteria for participating entities. By adjusting these parameters, the system can optimize the trade-off between security (through selective participation) and coordination overhead, simplifying management while maintaining distributed security benefits.
Data Source
AI summary
A method performed by a co-ordination network entity in a communications network includes transmitting a request message to a network registration entity in the communications network for identification information for a plurality of candidate network entities in the communications network capable of performing collaborative learning, and receiving identification information for the plurality of candidate network entities from the network registration entity. The method further includes initiating, at one or more network entities of the plurality of candidate network entities, training of a model using a machine-learning algorithm as part of a collaborative learning process.