10 citations · 12 across the 5 of their papers we have counts for
7 papers
Detection and Mitigation of Byzantine Attacks in Distributed Training
Konstantinos Konstantinidis, Namrata Vaswani, Aditya Ramamoorthy
A plethora of modern machine learning tasks require the utilization of large-scale distributed clusters as a critical component of the training pipeline. However, abnormal Byzantin…
Aspis: Robust Detection for Distributed Learning
Konstantinos Konstantinidis, Aditya Ramamoorthy
State-of-the-art machine learning models are routinely trained on large-scale distributed clusters. Crucially, such systems can be compromised when some of the computing devices ex…
ByzShield: An Efficient and Robust System for Distributed Training
Konstantinos Konstantinidis, Aditya Ramamoorthy
Training of large scale models on distributed clusters is a critical component of the machine learning pipeline. However, this training can easily be made to fail if some workers b…
Resolvable Designs for Speeding up Distributed Computing
Konstantinos Konstantinidis, Aditya Ramamoorthy
Distributed computing frameworks such as MapReduce are often used to process large computational jobs. They operate by partitioning each job into smaller tasks executed on differen…
CAMR: Coded Aggregated MapReduce
Konstantinos Konstantinidis, Aditya Ramamoorthy
Many big data algorithms executed on MapReduce-like systems have a shuffle phase that often dominates the overall job execution time. Recent work has demonstrated schemes where the…
Erasure coding for distributed matrix multiplication for matrices with bounded entries
Li Tang, Konstantinos Konstantinidis, Aditya Ramamoorthy
Distributed matrix multiplication is widely used in several scientific domains. It is well recognized that computation times on distributed clusters are often dominated by the slow…
Leveraging Coding Techniques for Speeding up Distributed Computing
Konstantinos Konstantinidis, Aditya Ramamoorthy
Large scale clusters leveraging distributed computing frameworks such as MapReduce routinely process data that are on the orders of petabytes or more. The sheer size of the data pr…