Straggler UE Detection in Federated Learning Air Interface
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Solution Overview
Problem
In federated learning over an air interface, straggler user equipment (UEs) delay model aggregation, leading to inefficient communication and resource waste, particularly in scenarios with numerous UEs and high iteration counts.
Innovation Solution
Identify and suspend communication with straggler UEs, allowing for faster model aggregation iterations by temporarily removing them from the network configuration, and resume communication when conditions improve.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the system waits for all UEs to complete model training and submission before proceeding with aggregation, then model convergence accuracy is improved, but training latency and iteration time increase significantly
Solution Approach 1:
The system dynamically adjusts the aggregation process by identifying and excluding straggler UEs that fail to submit models within a configured threshold time. This dynamic approach allows the system to proceed with aggregation using available models rather than statically waiting for all UEs, thereby reducing training latency while maintaining acceptable convergence accuracy through selective participation.
2Reliability
If the system continues to transmit aggregated models to straggler UEs, then model distribution completeness is improved, but network resource consumption and communication overhead increase
Solution Approach 1:
The system extracts and identifies straggler UEs based on their delayed model submission patterns and temporarily removes them from the active participant list. By taking out these stragglers from the communication loop, the system avoids wasting network resources on repeated transmissions to unresponsive devices while maintaining model distribution to active participants, thus reducing overall network resource consumption.
3Productivity
If the system suspends communication with identified straggler UEs, then training efficiency and iteration speed are improved, but risk of model divergence increases
Solution Approach 1:
The system applies partial action by suspending communication only with identified straggler UEs while continuing normal communication with active participants. This selective suspension allows the system to improve training efficiency by excluding delayed contributors without completely isolating affected users, thereby maintaining model convergence stability through continued engagement of the majority of active participants.
4Productivity
If the system implements straggler detection and suspension mechanisms, then iteration speed and resource utilization are improved, but system complexity and detection overhead increase
Solution Approach 1:
The system implements self-service by having UEs autonomously track their own model submission timing and automatically identify when they become stragglers based on predefined thresholds. Each UE monitors its own performance metrics and can self-report or self-suspend, reducing the need for complex centralized detection mechanisms and thereby limiting the increase in system complexity while still achieving improved iteration speed.
Data Source
AI summary
Detection of a straggler user equipment in federated learning over an air interface is provided. A method for detection of a straggler user equipment in federated learning may include identifying one or more straggler devices among a plurality of user devices and suspending transmission of an aggregated model to the one or more straggler devices for local model training. The method may also include resuming the transmission of the aggregated model to at least one of the one or more straggler devices for the local model training.