66 citations · 72 across the 10 of their papers we have counts for
10 papers
Leaky Wave Antennas for Next Generation Wireless Applications in sub-THz Frequencies: Current Status and Research Challenges
Natalie Lang, Atsutse K. Kludze, Nir Shlezinger +4
The ever-growing demand for ultra-high data rates, massive connectivity, and joint communication-sensing capabilities in future wireless networks is driving research into sub-terah…
OLALa: Online Learned Adaptive Lattice Codes for Heterogeneous Federated Learning
Natalie Lang, Maya Simhi, Nir Shlezinger
Federated learning (FL) enables collaborative training across distributed clients without sharing raw data, often at the cost of substantial communication overhead induced by trans…
Memory-Efficient Distributed Unlearning
Natalie Lang, Alon Helvitz, Nir Shlezinger
Machine unlearning considers the removal of the contribution of a set of data points from a trained model. In a distributed setting, where a server orchestrates training using data…
Adaptive Deadline and Batch Layered Synchronized Federated Learning
Asaf Goren, Natalie Lang, Nir Shlezinger +1
Federated learning (FL) enables collaborative model training across distributed edge devices while preserving data privacy, and typically operates in a round-based synchronous mann…
PAUSE: Low-Latency and Privacy-Aware Active User Selection for Federated Learning
Ori Peleg, Natalie Lang, Dan Ben Ami +3
Federated learning (FL) enables multiple edge devices to collaboratively train a machine learning model without the need to share potentially private data. Federated learning proce…
Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates
Natalie Lang, Alejandro Cohen, Nir Shlezinger
Synchronous federated learning (FL) is a popular paradigm for collaborative edge learning. It typically involves a set of heterogeneous devices locally training neural network (NN)…
Wideband THz Multi-User Downlink Communications with Leaky Wave Antennas
Natalie Lang, Yaela Gabay, Nir Shlezinger +4
Future wireless systems are envisioned to utilize the large spectra available at THz bands for wireless communications. Extremely massive multiple-input multiple-output (MIMO) ante…
Compressed Private Aggregation for Scalable and Robust Federated Learning over Massive Networks
Natalie Lang, Nir Shlezinger, Rafael G. L. D'Oliveira +1
Federated learning (FL) is an emerging paradigm that allows a central server to train machine learning models using remote users' data. Despite its growing popularity, FL faces cha…
Joint Privacy Enhancement and Quantization in Federated Learning
Natalie Lang, Elad Sofer, Tomer Shaked +1
Federated learning (FL) is an emerging paradigm for training machine learning models using possibly private data available at edge devices. The distributed operation of FL gives ri…
DeepUME: Learning the Universal Manifold Embedding for Robust Point Cloud Registration
Natalie Lang, Joseph M. Francos
Registration of point clouds related by rigid transformations is one of the fundamental problems in computer vision. However, a solution to the practical scenario of aligning spars…