Elena Mocanu

Quick and Robust Feature Selection: the Strength of Energy-efficient\n Sparse Training for Autoencoders

2020 · arXiv (Cornell University)

Major complications arise from the recent increase in the amount of\nhigh-dimensional data, including high computational costs and memory\nrequirements. Feature selection, which identifies the most relevant and\ninformative attributes of a dataset, has been introduced as a …

Addressing the Collaboration Dilemma in Low-Data Federated Learning via Transient Sparsity

2025 · arXiv (Cornell University)

Federated learning (FL) enables collaborative model training across decentralized clients while preserving data privacy, leveraging aggregated updates to build robust global models. However, this training paradigm faces significant challenges due to data heterogeneity and limited …