Federated Learning Substitution for Interrupted Model Training
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Solution Overview
Problem
Existing federated learning systems experience interruptions during model training due to various constraints such as minimum quality of Uu links, computation resource availability, power resource availability, and security/integrity levels, which affect the training process and efficiency.
Innovation Solution
The system selects substitute apparatus based on data distribution similarity, location, proximity, mobility patterns, and communication quality to configure and train local models, using substitute training configurations and global models to ensure uninterrupted training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If substitute apparatus are selected and configured for training local models, then training continuity and reliability are improved, but system complexity and configuration overhead increase
Solution Approach 1:
The system pre-selects and pre-configures substitute apparatus before training interruptions occur. The network device identifies candidate substitute apparatus in advance and establishes their training configurations beforehand, so that when an apparatus becomes unavailable, the substitution can occur immediately without delay or complex real-time decision-making.
Solution Approach 2:
The network device acts as an intermediary that manages the selection and configuration of substitute apparatus. It receives availability information from apparatus, determines appropriate substitutes based on pre-established criteria, and coordinates the training task redistribution, thereby simplifying the overall system complexity by centralizing the substitution logic.
2Productivity
If multiple apparatus are selected for parallel training, then training efficiency and productivity are improved, but resource consumption and coordination overhead increase
Solution Approach 1:
The system selects a subset of apparatus for parallel training rather than involving all available apparatus. The network device determines the optimal number of apparatus to engage based on training requirements and resource availability, avoiding unnecessary resource consumption while maintaining training efficiency. The substitute apparatus are only activated when needed, not continuously.
3Reliability
If strict quality and security constraints are enforced, then training reliability is improved, but training speed and adaptability decrease
Solution Approach 1:
The system dynamically adjusts training configurations based on real-time conditions. The network device monitors apparatus availability, data distribution similarity, and training progress, then adapts the selection of substitute apparatus and their configurations accordingly. This allows the system to maintain reliability constraints while optimizing training speed by using less stringent configurations when appropriate.
Solution Approach 2:
The system changes key parameters such as data distribution similarity thresholds and apparatus selection criteria based on training stage and resource availability. During critical training phases, stricter parameters are enforced to ensure reliability, while during less critical phases or when resources are constrained, more flexible parameters are used to maintain training speed.
Data Source
AI summary
An apparatus configured to train a model in a communications network using federated learning, the apparatus comprising means for: selecting at least two further apparatus for training a local model; further selecting a substitute apparatus for at least one of the at least two selected further apparatus; and configuring each of the at least two further apparatus for training the local model and configuring the substitute apparatus for the at least one of the two selected further apparatus for training the local model; receiving a local training result from at least one of the at least two further apparatus and a local training result from the substitute apparatus for the at least one of the two selected further apparatus; and combining the local training results to generate aggregated training results for the model.