3 papers
eess.SP2025
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…
eess.SP2025
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…
cs.LG2024
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)…