Distributed Machine Learning Training

Description

Distributed training is a machine learning (ML) training approach defined in 3GPP specifications 28.105 and 28.858, introduced in Release 19. It refers to the process of distributing the training workload across multiple ML training functions (MLTFs), which are logical entities within the 3GPP management system responsible for training ML models. Instead of relying on a single, centralized training function, distributed training leverages multiple training functions that may reside in different network elements, management domains, or even across administrative boundaries. This approach is essential for handling large-scale datasets, reducing training time, and enabling privacy-preserving or locality-aware model training in telecommunications networks.

The architecture for distributed training in 3GPP is built around the Management Data Analytics (MDA) and ML management framework. The ML training function is a functional entity that performs model training, and in a distributed setup, multiple such functions collaborate. The coordination is typically managed by a central entity, such as an ML training coordinator or a management service producer, which orchestrates the training process. The workload distribution can follow various paradigms, including data-parallel training (where each function trains on a subset of data and gradients are aggregated), model-parallel training (where different parts of the model are trained on different functions), or a hybrid approach. 3GPP specifications define the interfaces and procedures for exchanging training-related information, such as training data, model parameters, and performance metrics, between these functions.

Key components of distributed training include the ML training functions themselves, the training coordinator, and the data sources. The ML training functions may be deployed at different levels: within a single network element (e.g., a gNB), across multiple network elements, or in a hierarchical manner (e.g., edge and central cloud). The training coordinator is responsible for task scheduling, resource allocation, and synchronization. Data sources provide the training data, which may be locally available at each training function or accessed via management services. The 3GPP management framework (TS 28.105) specifies the ML management capabilities, including training, testing, and inference, while TS 28.858 provides details on the ML training procedures and interfaces.

How it works: In a typical distributed training scenario, the training coordinator initiates a training job and divides the workload among participating ML training functions. Each function receives a portion of the training data or a subset of the model and performs local training. During training, functions may exchange model updates (e.g., gradients or parameters) periodically. The coordinator aggregates these updates to form a global model, which is then redistributed for the next iteration. This process repeats until convergence. Communication between functions can be synchronous or asynchronous, depending on the requirements. 3GPP defines the necessary management services, such as the ML Training Service, which allows a consumer to request training and a producer to execute it, potentially in a distributed manner.

Distributed training plays a crucial role in the network by enabling AI/ML-based network optimization, automation, and analytics. It allows operators to train models using data from multiple network domains without centralizing sensitive data, thus addressing privacy and regulatory concerns. It also improves scalability and reduces training time by parallelizing computations. As networks evolve towards 6G, distributed training will be fundamental for enabling collaborative intelligence across network elements, supporting use cases like network slicing, energy efficiency, and fault prediction. The 3GPP specifications ensure interoperability and standardized management of these distributed training processes, facilitating multi-vendor deployments.

Purpose & Motivation

Distributed training exists to address the limitations of centralized ML training in telecommunications networks. Centralized training requires collecting all training data at a single location, which can be impractical due to data volume, privacy regulations, and network bandwidth constraints. It also creates a single point of failure and can be slow for large models. Distributed training solves these problems by spreading the workload across multiple ML training functions, enabling parallel processing, reducing training time, and keeping data local where possible.

Historically, 3GPP introduced ML management capabilities in Release 17 and 18, focusing on centralized training and inference. However, as networks became more complex and data-intensive, the need for scalable training became evident. Release 19 formally defined distributed training as part of the ML management framework, recognizing that future networks will require collaborative training across multiple domains, including RAN, core, and management. This approach also aligns with edge computing trends, where training can occur closer to data sources.

The motivation for distributed training includes: (1) scalability – handling massive datasets generated by network elements; (2) efficiency – reducing training time through parallelism; (3) privacy – keeping sensitive data within local domains; (4) resilience – avoiding single points of failure; and (5) regulatory compliance – adhering to data sovereignty requirements. By standardizing distributed training, 3GPP ensures that multi-vendor networks can interoperate and that AI/ML can be seamlessly integrated into network operations.