Federated Learning Client Selection Using 5G Analytics
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Solution Overview
Problem
Existing federated learning systems face challenges in selecting and managing mobile device participation due to variable wireless channel conditions, device heterogeneity, and limited storage, leading to inefficiencies in training convergence and latency management.
Innovation Solution
A method and apparatus for joint selection of ML servers and client members using extended analytics IDs, considering communication performance and mobility, to optimize federated learning operations in 5G systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If all mobile devices are included in federated learning, then training data diversity is improved, but system complexity and management difficulty increase due to device heterogeneity and variable wireless channel conditions
Solution Approach 1:
The patent segments the mobile device population into selected and non-selected groups for federated learning participation. The network dynamically selects a subset of devices based on analytics data, separating them from the complete device population. This segmentation enables manageable system complexity while maintaining diverse training data through strategic device selection.
Solution Approach 2:
The patent changes the participation parameter from inclusion of all devices to selective inclusion based on analytics. By dynamically adjusting which devices participate in each training round based on channel conditions, device capabilities, and data characteristics, the system optimizes the balance between data diversity and management complexity.
2Productivity
If dynamic device selection is implemented, then training efficiency is improved, but communication overhead and analytics processing requirements increase
Solution Approach 1:
The patent implements self-service through analytics-driven automatic device selection. The network autonomously collects analytics data, evaluates device suitability, and makes selection decisions without manual intervention. This automation improves training efficiency by rapidly identifying optimal participants while managing communication overhead through targeted data collection and processing.
Solution Approach 2:
The patent employs feedback mechanisms where analytics data about device performance, channel conditions, and training progress is continuously collected and used to adjust device selection for subsequent training rounds. This feedback loop enables the system to learn from previous performance and optimize future selections, improving efficiency while managing communication requirements through intelligent data utilization.
3Measurement precision
If extended analytics collection is performed, then selection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by collecting and preparing analytics data in advance before device selection is needed. The network pre-processes channel condition measurements, device capability assessments, and historical performance data, making them ready for rapid selection decisions. This advance preparation improves selection accuracy while reducing real-time processing time requirements.
Solution Approach 2:
The patent uses partial action by selecting only the essential analytics parameters needed for effective device selection rather than collecting all possible device metrics. This selective approach maintains sufficient selection accuracy while significantly reducing processing time and computational resource requirements compared to comprehensive data collection.
Data Source
AI summary
Embodiments described herein relate to methods and apparatuses for selection of one or more ML client members from a plurality of potential ML client members to perform federated learning. A method in an application function comprises responsive to commencement of the federated learning, obtaining first analytics information relating to communication performance between potential groups of ML servers and a plurality of potential ML client members; and selecting, based on the first analytics information, a first group of ML client members to perform the federated learning from the plurality of potential ML client members.