250 citations · 1.1k across the 76 of their papers we have counts for
9 papers · 1 filter
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…
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…
A Model for Learned Bloom Filters, and Optimizing by Sandwiching
Michael Mitzenmacher
Recent work has suggested enhancing Bloom filters by using a pre-filter, based on applying machine learning to determine a function that models the data set the Bloom filter is mea…
Weightless: Lossy Weight Encoding For Deep Neural Network Compression
Brandon Reagen, Udit Gupta, Robert Adolf +4
The large memory requirements of deep neural networks limit their deployment and adoption on many devices. Model compression methods effectively reduce the memory requirements of t…