UE Capability Reporting and Grouping for Federated Learning Delays
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Solution Overview
Problem
Existing wireless communication systems face inefficiencies in federated learning processes due to varying machine learning capabilities and turnaround times among user equipment (UEs), leading to potential delays and bottlenecks in gradient updates, which affect the convergence time of the training procedure.
Innovation Solution
User equipment (UEs) report their machine learning processing capabilities and approximate turnaround times to the base station, enabling the base station to group UEs based on their capabilities, ensuring timely and efficient participation in federated learning rounds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If UEs with varying machine learning capabilities participate in federated learning rounds without grouping, then all UEs can potentially contribute to gradient updates, but slower UEs cause delays and bottlenecks in the training procedure
Solution Approach 1:
The patent segments UEs into different groups based on their machine learning processing capabilities and turnaround times. The base station receives capability reports from UEs and forms capability groups, where each group contains UEs with similar processing speeds. This segmentation allows the system to schedule gradient collection rounds appropriately for each group, preventing slower UEs from becoming bottlenecks while maintaining overall system productivity.
2Reliability
If the base station waits for all UEs to complete gradient computations before proceeding, then no gradient updates are lost, but the convergence time increases due to slower UEs
Solution Approach 1:
The patent implements dynamic scheduling where the base station adjusts the number of rounds and timing of gradient collections based on reported UE capabilities. Rather than using a static wait-for-all approach, the system dynamically forms capability groups and schedules gradient collections to match each group's processing speed. This dynamic adaptation maintains reliability by ensuring gradients are collected when ready while minimizing convergence time by not waiting for slower UEs to complete their computations.
3Adaptability or versatility
If UEs report detailed machine learning capabilities, then the base station can optimize grouping and scheduling, but the signaling overhead and complexity increase
Solution Approach 1:
The patent changes the parameter representation of UE capabilities by having UEs report standardized capability metrics (such as processing speed categories and turnaround time estimates) rather than detailed hardware specifications. This parameter transformation enables the base station to perform effective capability-based grouping and scheduling while keeping the reporting mechanism simple and the signaling overhead manageable. The capability reports use quantized or categorized parameters that are easy to process and compare.
Data Source
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AI summary
A method of wireless communication by a user equipment (UE) receives a machine learning model from a base station. The UE reports, to the base station, a machine learning processing capability. The UE also transmits, to the base station, gradient updates or weight updates to the machine learning model. A base station transmits a machine learning model to a number of UEs. The base station receives, from each of the number of UEs, a machine learning processing capability report. The base station groups a number of UEs in accordance with the machine learning processing capability reports, to receive gradient updates to the machine learning model.