38 citations · 86 across the 9 of their papers we have counts for
9 papers
Apodotiko: Enabling Efficient Serverless Federated Learning in Heterogeneous Environments
Mohak Chadha, Alexander Jensen, Jianfeng Gu +2
Federated Learning (FL) is an emerging machine learning paradigm that enables the collaborative training of a shared global model across distributed clients while keeping the data…
Training Heterogeneous Client Models using Knowledge Distillation in Serverless Federated Learning
Mohak Chadha, Pulkit Khera, Jianfeng Gu +2
Federated Learning (FL) is an emerging machine learning paradigm that enables the collaborative training of a shared global model across distributed clients while keeping the data…
gFaaS: Enabling Generic Functions in Serverless Computing
Mohak Chadha, Paul Wieland, Michael Gerndt
With the advent of AWS Lambda in 2014, Serverless Computing, particularly Function-as-a-Service (FaaS), has witnessed growing popularity across various application domains. FaaS en…
GreenCourier: Carbon-Aware Scheduling for Serverless Functions
Mohak Chadha, Thandayuthapani Subramanian, Eishi Arima +3
This paper presents GreenCourier, a novel scheduling framework that enables the runtime scheduling of serverless functions across geographically distributed regions based on their…
Sustainability in HPC: Vision and Opportunities
Mohak Chadha, Eishi Arima, Amir Raoofy +2
Tackling climate change by reducing and eventually eliminating carbon emissions is a significant milestone on the path toward establishing an environmentally sustainable society. A…
FaST-GShare: Enabling Efficient Spatio-Temporal GPU Sharing in Serverless Computing for Deep Learning Inference
Jianfeng Gu, Yichao Zhu, Puxuan Wang +2
Serverless computing (FaaS) has been extensively utilized for deep learning (DL) inference due to the ease of deployment and pay-per-use benefits. However, existing FaaS platforms…