Radu Mărculescu

FedMAX: Mitigating Activation Divergence for Accurate and Communication-Efficient Federated Learning

2020 · arXiv (Cornell University)

In this paper, we identify a new phenomenon called activation-divergence which occurs in Federated Learning (FL) due to data heterogeneity (i.e., data being non-IID) across multiple users. Specifically, we argue that the activation vectors in …

Statistical learning in chip (SLIC)

2015

Despite best efforts, integrated systems are “born” (manufactured) with a unique `personality' that stems from our inability to precisely fabricate their underlying circuits, and create software a priori for controlling the resulting uncertainty. It is …