SMLT: A Serverless Framework for Scalable and Adaptive Machine Learning Design and Training

no code implementations • 4 May 2022 • Ahsan Ali, Syed Zawad, Paarijaat Aditya, Istemi Ekin Akkus, Ruichuan Chen, Feng Yan

In addition, by providing an end-to-end design, SMLT solves the intrinsic problems in serverless platforms such as the communication overhead, limited function execution duration, need for repeated initialization, and also provides explicit fault tolerance for ML training.

BIG-bench Machine Learning Management +1

Citadel: Protecting Data Privacy and Model Confidentiality for Collaborative Learning with SGX

no code implementations • 4 May 2021 • Chengliang Zhang, Junzhe Xia, Baichen Yang, Huancheng Puyang, Wei Wang, Ruichuan Chen, Istemi Ekin Akkus, Paarijaat Aditya, Feng Yan

This paper presents Citadel, a scalable collaborative ML system that protects the privacy of both data owner and model owner in untrusted infrastructures with the help of Intel SGX.

Federated Learning