11 citations · 23 across the 9 of their papers we have counts for
5 papers · 1 filter
DynSL: Resource-Adaptive Model Training across Heterogeneous Edge Devices
Boris Radovič, Istemi Ekin Akkus, Marco Canini +4
Training models over data distributed across edge devices presents a trade-off: centralizing data simplifies training but raises privacy and regulatory concerns, while full local t…
Protecting Confidentiality, Privacy and Integrity in Collaborative Learning
Dong Chen, Alice Dethise, Istemi Ekin Akkus +6
A collaboration between dataset owners and model owners is needed to facilitate effective machine learning (ML) training. During this collaboration, however, dataset owners and mod…
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…
Approximate Distributed Joins in Apache Spark
Do Le Quoc, Istemi Ekin Akkus, Pramod Bhatotia +4
The join operation is a fundamental building block of parallel data processing. Unfortunately, it is very resource-intensive to compute an equi-join across massive datasets. The ap…
Sieve: Actionable Insights from Monitored Metrics in Microservices
Jörg Thalheim, Antonio Rodrigues, Istemi Ekin Akkus +5
Major cloud computing operators provide powerful monitoring tools to understand the current (and prior) state of the distributed systems deployed in their infrastructure. While suc…