Our work on cloud performance management was recently awarded the Test-of-Time award at the ACM/IFIP Middleware Conference held this December at Vanderbilt University. The award is given for a paper that appeared at the conference 10 years or so back and that is considered to have had the greatest impact on the field, be this […]
Category: Paper digest
Can You Learn Complex Models from Leaked Data?
This post is to give the key insights from our just-accepted CVPR 2024 paper: “Leak and Learn: An Attacker’s Cookbook to Train Using Leaked Data from Federated Learning” Joshua Christian Zhao, Ahaan Dabholkar, Atul Sharma, and Saurabh Bagchi Federated learning (FL) is a decentralized learning paradigm introduced to preserve privacy of client data. Despite this, […]
Federated Learning: How Private Is It Really?
Co-authored with Arash Nourian, Director at AWS AI Federated Learning (FL) is a widely popular structure that allows one to learn a Machine Learning (ML) model collaboratively. The classical structure of FL is that there are multiple clients each with their own local data, which they would possibly like to keep private, and there is […]
To Fuse Wisely in Serverless DAGs: A Sigmetrics Winner
This post gives a high-level view of our Sigmetrics 2022 paper, which was recently announced at the conference as the best paper winner. Ashraf Mahgoub, Edgardo Barsallo Yi (Purdue University), Karthick Shankar (CMU), Eshaan Minocha (Purdue University), Sameh Elnikety (Microsoft Research), Saurabh Bagchi, and Somali Chaterji (Purdue University). WISEFUSE: Workload Characterization and DAG Transformation for […]
LiteReconfig at Eurosys 2022: Cost and Content-Aware Video Object Detection for Mobile GPUs
Object detection is arguably one of central problems in computer vision. Much progress has been made over the past few years in deep learning based object detectors. Despite their impressive accuracy results on standard benchmarks, these models come at a price of their complexity and computational cost. This imposes a major barrier to deploy these […]
SONIC: The Serverless Data Corraller
This is a high-level view of our work on serverless computing that has just been accepted to Usenix ATC 2021, plus some historical context for why we are where we are. And a look ahead at the rich problems that we still have to tame. Ashraf Mahgoub (Purdue University), Karthick Shankar (Carnegie Mellon University), Subrata […]
Distant Whispers from an Academic Engineer's World 