19 citations · 29 across the 5 of their papers we have counts for
5 papers
SMLT: A Serverless Framework for Scalable and Adaptive Machine Learning Design and Training
Ahsan Ali, Syed Zawad, Paarijaat Aditya +3
In today's production machine learning (ML) systems, models are continuously trained, improved, and deployed. ML design and training are becoming a continuous workflow of various t…
The Age of Correlated Features in Supervised Learning based Forecasting
Md Kamran Chowdhury Shisher, Heyang Qin, Lei Yang +2
In this paper, we analyze the impact of information freshness on supervised learning based forecasting. In these applications, a neural network is trained to predict a time-varying…
Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning
Syed Zawad, Ahsan Ali, Pin-Yu Chen +5
Data heterogeneity has been identified as one of the key features in federated learning but often overlooked in the lens of robustness to adversarial attacks. This paper focuses on…
InfiniCache: Exploiting Ephemeral Serverless Functions to Build a Cost-Effective Memory Cache
Ao Wang, Jingyuan Zhang, Xiaolong Ma +6
Internet-scale web applications are becoming increasingly storage-intensive and rely heavily on in-memory object caching to attain required I/O performance. We argue that the emerg…
TiFL: A Tier-based Federated Learning System
Zheng Chai, Ahsan Ali, Syed Zawad +7
Federated Learning (FL) enables learning a shared model across many clients without violating the privacy requirements. One of the key attributes in FL is the heterogeneity that ex…