Core Concepts
Layer-wise aggregation in SALF improves FL performance under tight latency constraints.
Abstract
The content introduces Stragglers-Aware Layer-Wise Federated Learning (SALF) as a solution to the challenges of synchronous federated learning (FL) in the presence of stragglers. SALF leverages layer-wise model updates to improve performance under tight latency constraints. The article discusses the impact of system heterogeneity on FL latency and proposes SALF as a method to address these challenges. The theoretical analysis and empirical observations demonstrate the effectiveness of SALF compared to alternative mechanisms. The content is structured into sections covering the introduction, system model, SALF algorithm, analysis, experimental study, and conclusions.
Introduction
FL for edge learning with stragglers sensitivity.
Challenges of FL latency in dynamic environments.
System Model
Central server training model with user data.
FL operation in rounds with global model updates.
SALF Algorithm
Layer-wise aggregation for straggler-aware FL.
Users' local training and server aggregation process.
Analysis
Theoretical convergence guarantees for SALF.
Assumptions and statistical modeling of stragglers.
Experimental Study
Evaluation of SALF on handwritten digit recognition.
Performance comparison with other FL methods.
Test accuracy results for different stragglers percentages.
Conclusions
SALF improves FL performance under tight latency constraints.
Discussion on the effectiveness of SALF in mitigating stragglers' impact.
Stats
SALF converges at the same rate as FL with no timing limitations.
Stragglers contribute to the aggregation of intermediate layers in SALF.
Quotes
"SALF converges at the same asymptotic rate as FL with no timing limitations."
"Stragglers contribute to the aggregation of intermediate layers in SALF."