DeepUME: Learning the Universal Manifold Embedding for Robust Point Cloud Registration. - Natalie Lang; Joseph M. Francos (BMVC, 2021)

Joint Privacy Enhancement and Quantization in Federated Learning. - Natalie Lang; Nir Shlezinger (ISIT, 2022)

CPA: Compressed Private Aggregation for Scalable Federated Learning Over Massive Networks. - Natalie Lang; Elad Sofer; Nir Shlezinger; Rafael G. L. D'Oliveira; Salim El Rouayheb (ICASSP, 2023)

Compressed Private Aggregation for Scalable and Robust Federated Learning over Massive Networks. - Natalie Lang; Nir Shlezinger; Rafael G. L. D'Oliveira; Salim El Rouayheb (CoRR, 2023)

Joint Privacy Enhancement and Quantization in Federated Learning. - Natalie Lang; Elad Sofer; Tomer Shaked; Nir Shlezinger (IEEE Trans. Signal Process., 2023)

Data-Driven Lattices for Vector Quantization. - Natalie Lang; Itamar Assaf; Omer Bokobza; Nir Shlezinger (ICASSP, 2024)

PAUSE: Privacy-Aware Active User Selection for Federated Learning. - Ori Peleg; Natalie Lang; Stefano Rini; Nir Shlezinger; Kobi Cohen (ICASSP, 2025)

PAUSE: Low-Latency and Privacy-Aware Active User Selection for Federated Learning. - Ori Peleg; Natalie Lang; Stefano Rini; Nir Shlezinger; Kobi Cohen (CoRR, 2025)

Adaptive Deadline and Batch Layered Synchronized Federated Learning. - Asaf Goren; Natalie Lang; Nir Shlezinger; Alejandro Cohen (CoRR, 2025)

Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates. - Natalie Lang; Alejandro Cohen; Nir Shlezinger (IEEE Trans. Commun., 2025)

OLALa: Online Learned Adaptive Lattice Codes for Heterogeneous Federated Learning. - Natalie Lang; Maya Simhi; Nir Shlezinger (CoRR, 2025)