36 citations · 111 across the 13 of their papers we have counts for
13 papers
Entropy-Constrained Adaptive Stochastic Quantization
Ran Ben Basat, Yaniv Ben-Itzhak, Michael Mitzenmacher +1
Adaptive stochastic quantization (ASQ) is a recently introduced quantization approach that optimizes the Mean Squared Error (MSE) for a given input while preserving unbiasedness. I…
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…
Optimal and Near-Optimal Adaptive Vector Quantization
Ran Ben-Basat, Yaniv Ben-Itzhak, Michael Mitzenmacher +1
Quantization is a fundamental optimization for many machine-learning use cases, including compressing gradients, model weights and activations, and datasets. The most accurate form…
DoCoFL: Downlink Compression for Cross-Device Federated Learning
Ron Dorfman, Shay Vargaftik, Yaniv Ben-Itzhak +1
Many compression techniques have been proposed to reduce the communication overhead of Federated Learning training procedures. However, these are typically designed for compressing…
ScionFL: Efficient and Robust Secure Quantized Aggregation
Yaniv Ben-Itzhak, Helen Möllering, Benny Pinkas +7
Secure aggregation is commonly used in federated learning (FL) to alleviate privacy concerns related to the central aggregator seeing all parameter updates in the clear. Unfortunat…
IIsy: Practical In-Network Classification
Changgang Zheng, Zhaoqi Xiong, Thanh T Bui +6
The rat race between user-generated data and data-processing systems is currently won by data. The increased use of machine learning leads to further increase in processing require…
Automating In-Network Machine Learning
Changgang Zheng, Mingyuan Zang, Xinpeng Hong +4
Using programmable network devices to aid in-network machine learning has been the focus of significant research. However, most of the research was of a limited scope, providing a…
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…
CloudCast: Characterizing Public Clouds Connectivity
Noga H. Rotman, Yaniv Ben-Itzhak, Aran Bergman +4
Public clouds are one of the most thriving technologies of the past decade. Major applications over public clouds require world-wide distribution and large amounts of data exchange…
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…
RADE: Resource-Efficient Supervised Anomaly Detection Using Decision Tree-Based Ensemble Methods
Shay Vargaftik, Isaac Keslassy, Ariel Orda +1
Decision-tree-based ensemble classification methods (DTEMs) are a prevalent tool for supervised anomaly detection. However, due to the continued growth of datasets, DTEMs result in…
C-Share: Optical Circuits Sharing for Software-Defined Data-Centers
Yaniv Ben-Itzhak, Cosmin Caba, Liran Schour +1
Integrating optical circuit switches in data-centers is an on-going research challenge. In recent years, state-of-the-art solutions introduce hybrid packet/circuit architectures fo…