14 citations · 41 across the 9 of their papers we have counts for
11 papers
Bounded Memory in Distributed Networks
Ran Ben Basat, Keren Censor-Hillel, Yi-Jun Chang +3
The recent advent of programmable switches makes distributed algorithms readily deployable in real-world datacenter networks. However, there are still gaps between theory and pract…
Counter Pools: Counter Representation for Efficient Stream Processing
Ran Ben Basat, Gil Einziger, Bilal Tyah +1
Due to the large data volume and number of distinct elements, space is often the bottleneck of many stream processing systems. The data structures used by these systems often consi…
THC: Accelerating Distributed Deep Learning Using Tensor Homomorphic Compression
Minghao Li, Ran Ben Basat, Shay Vargaftik +4
Deep neural networks (DNNs) are the de facto standard for essential use cases, such as image classification, computer vision, and natural language processing. As DNNs and datasets…
QUIC-FL: Quick Unbiased Compression for Federated Learning
Ran Ben Basat, Shay Vargaftik, Amit Portnoy +3
Distributed Mean Estimation (DME), in which clients communicate vectors to a parameter server that estimates their average, is a fundamental building block in communication-eff…
Zero-CPU Collection with Direct Telemetry Access
Jonatan Langlet, Ran Ben Basat, Sivaramakrishnan Ramanathan +4
Programmable switches are driving a massive increase in fine-grained measurements. This puts significant pressure on telemetry collectors that have to process reports from many swi…
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.…