240 citations · 571 across the 28 of their papers we have counts for
10 papers · 1 filter
OpenG2G: A Simulation Platform for AI Datacenter-Grid Runtime Coordination
Jae-Won Chung, Zhirui Liang, Yanyong Mao +3
AI's growing compute demand and new datacenter buildouts present major capacity and reliability challenges for the electricity grid, leading to multi-year interconnection delays fo…
Chasing Low-Carbon Electricity for Practical and Sustainable DNN Training
Zhenning Yang, Luoxi Meng, Jae-Won Chung +1
Deep learning has experienced significant growth in recent years, resulting in increased energy consumption and carbon emission from the use of GPUs for training deep neural networ…
FLINT: A Platform for Federated Learning Integration
Ewen Wang, Ajay Kannan, Yuefeng Liang +2
Cross-device federated learning (FL) has been well-studied from algorithmic, system scalability, and training speed perspectives. Nonetheless, moving from centralized training to c…
Auxo: Efficient Federated Learning via Scalable Client Clustering
Jiachen Liu, Fan Lai, Yinwei Dai +3
Federated learning (FL) is an emerging machine learning (ML) paradigm that enables heterogeneous edge devices to collaboratively train ML models without revealing their raw data to…
Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training
Jie You, Jae-Won Chung, Mosharaf Chowdhury
Training deep neural networks (DNNs) is becoming increasingly more resource- and energy-intensive every year. Unfortunately, existing works primarily focus on optimizing DNN traini…
Swan: A Neural Engine for Efficient DNN Training on Smartphone SoCs
Sanjay Sri Vallabh Singapuram, Fan Lai, Chuheng Hu +1
The need to train DNN models on end-user devices (e.g., smartphones) is increasing with the need to improve data privacy and reduce communication overheads. Unlike datacenter serve…
Egeria: Efficient DNN Training with Knowledge-Guided Layer Freezing
Yiding Wang, Decang Sun, Kai Chen +2
Training deep neural networks (DNNs) is time-consuming. While most existing solutions try to overlap/schedule computation and communication for efficient training, this paper goes…
The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning
Raed Kontar, Naichen Shi, Xubo Yue +12
The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the future, IoFT, the cloud will be substituted by the crowd where model training is br…
FedScale: Benchmarking Model and System Performance of Federated Learning at Scale
Fan Lai, Yinwei Dai, Sanjay S. Singapuram +4
We present FedScale, a federated learning (FL) benchmarking suite with realistic datasets and a scalable runtime to enable reproducible FL research. FedScale datasets encompass a w…
Oort: Efficient Federated Learning via Guided Participant Selection
Fan Lai, Xiangfeng Zhu, Harsha V. Madhyastha +1
Federated Learning (FL) is an emerging direction in distributed machine learning (ML) that enables in-situ model training and testing on edge data. Despite having the same end goal…