FLHetBench: Benchmarking Device and State Heterogeneity in Federated Learning
Federated learning (FL) is a powerful technology that enables collaborative training of
machine learning models without sharing private data among clients. The fundamental …
machine learning models without sharing private data among clients. The fundamental …
FedTrans: Efficient Federated Learning via Multi-Model Transformation
Y Zhu, J Liu, M Chowdhury… - Proceedings of Machine …, 2024 - proceedings.mlsys.org
Federated learning (FL) aims to train machine learning (ML) models across potentially
millions of edge client devices. Yet, training and customizing models for FL clients is …
millions of edge client devices. Yet, training and customizing models for FL clients is …
Workload-Aware Hardware Accelerator Mining for Distributed Deep Learning Training
In this paper, we present a novel technique to search for hardware architectures of
accelerators optimized for end-to-end training of deep neural networks (DNNs). Our …
accelerators optimized for end-to-end training of deep neural networks (DNNs). Our …
Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates
Synchronous federated learning (FL) is a popular paradigm for collaborative edge learning.
It typically involves a set of heterogeneous devices locally training neural network (NN) …
It typically involves a set of heterogeneous devices locally training neural network (NN) …
FedTrans: Efficient Federated Learning Over Heterogeneous Clients via Model Transformation
Federated learning (FL) aims to train machine learning (ML) models across potentially
millions of edge client devices. Yet, training and customizing models for FL clients is …
millions of edge client devices. Yet, training and customizing models for FL clients is …