10 citations · 24 across the 8 of their papers we have counts for
8 papers
A Note on TurboQuant and the Earlier DRIVE/EDEN Line of Work
Ran Ben-Basat, Yaniv Ben-Itzhak, Gal Mendelson +3
This note clarifies the relationship between the recent TurboQuant work and the earlier DRIVE (NeurIPS 2021) and EDEN (ICML 2022) schemes. DRIVE is a 1-bit quantizer that EDEN exte…
Learning Service Slowdown using Observational Data
Xu Kuang, Gal Mendelson
Being able to identify service slowdowns is crucial to many operational problems. We study how to use observational congestion data to learn service slowdown in a multi-server syst…
Load Balancing Using Sparse Communication
Gal Mendelson, Xu Kuang
Load balancing across parallel servers is an important class of congestion control problems that arises in service systems. An effective load balancer relies heavily on accurate, r…
EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning
Shay Vargaftik, Ran Ben Basat, Amit Portnoy +3
Distributed Mean Estimation (DME) is a central building block in federated learning, where clients send local gradients to a parameter server for averaging and updating the model.…
DRIVE: One-bit Distributed Mean Estimation
Shay Vargaftik, Ran Ben Basat, Amit Portnoy +3
We consider the problem where clients transmit -dimensional real-valued vectors using bits each, in a manner that allows the receiver to approximately reconstruc…
On the Persistent-Idle Load Distribution Policy Under Batch Arrivals and Random Service Capacity
Rami Atar, Isaac Keslassy, Gal Mendelson +2
The Persistent-Idle (PI) load distribution policy was recently introduced as an appealing alternative to current low-communication load balancing techniques. In PI, servers only up…