Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates

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."

Key Insights Distilled From

Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates

by Natalie Lang... at arxiv.org 03-28-2024

https://arxiv.org/pdf/2403.18375.pdf

Deeper Inquiries

How can SALF be adapted for different learning algorithms

SALF can be adapted for different learning algorithms by modifying the aggregation rule to suit the specific optimization procedure of the algorithm. For instance, if the learning algorithm does not follow the backpropagation method commonly used in neural networks, the aggregation step in SALF would need to be adjusted accordingly. Additionally, the layer-wise approach of SALF can be tailored to accommodate the specific characteristics and requirements of different learning algorithms. This adaptation may involve redefining how partial updates are conveyed and aggregated, as well as considering the unique convergence properties of the algorithm in the context of federated learning.

What are the implications of system heterogeneity on FL performance

System heterogeneity has significant implications on FL performance. The presence of heterogeneous edge devices with varying computational resources and availability can lead to stragglers, which are users that are unable to complete their local computations within the specified deadline. Stragglers can significantly impact the overall training process by delaying the aggregation of model updates, thereby increasing latency and reducing throughput. This can result in degraded model performance, especially in applications with tight latency constraints. System heterogeneity also introduces challenges in resource allocation, scheduling, and model convergence, requiring specialized techniques like SALF to mitigate the effects of stragglers and ensure efficient federated learning.

How can SALF be extended to handle multiple iterations in FL training

To handle multiple iterations in FL training, SALF can be extended by incorporating a mechanism for tracking and managing the progress of each iteration across different rounds. This extension would involve maintaining state information about the progress of each user's local training, allowing for the synchronization of multiple iterations within the specified latency constraints. By coordinating the timing of model updates and aggregations across multiple iterations, SALF can ensure that the global model converges efficiently while accommodating the varying computational capabilities and availability of the participating devices. This extension would enhance the scalability and adaptability of SALF for FL scenarios requiring multiple training iterations.