127 citations · 135 across the 9 of their papers we have counts for
10 papers
A Practical Recipe for Semi-Supervised Federated ASR: Online Pseudo-Labels with Server Update Stabilization
Wonho Bae, Zakaria Aldeneh, Martin Pelikan +3
Semi-supervised federated learning (SSFL) trains models on clients' unlabeled data using a teacher to generate pseudo-labels, with a small labeled seed dataset on the server. Autom…
B2B Customer Conversion Prediction: A Document Representation, Graph Theory, and CatBoost Driven Methodology
Tianqi Wang, Sheikh Shams Azam, Wan Eih Huang +3
In the one-time selling B2B context, the buying cycle may last months or even years. During the long process, targeting customers that have a high potential to make purchases and r…
Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers and Gradient Clipping
Martin Pelikan, Sheikh Shams Azam, Vitaly Feldman +4
While federated learning (FL) and differential privacy (DP) have been extensively studied, their application to automatic speech recognition (ASR) remains largely unexplored due to…
Importance of Smoothness Induced by Optimizers in FL4ASR: Towards Understanding Federated Learning for End-to-End ASR
Sheikh Shams Azam, Tatiana Likhomanenko, Martin Pelikan +1
In this paper, we start by training End-to-End Automatic Speech Recognition (ASR) models using Federated Learning (FL) and examining the fundamental considerations that can be pivo…
Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?
Sheikh Shams Azam, Seyyedali Hosseinalipour, Qiang Qiu +1
In this paper, we question the rationale behind propagating large numbers of parameters through a distributed system during federated learning. We start by examining the rank chara…
Federated Learning Beyond the Star: Local D2D Model Consensus with Global Cluster Sampling
Frank Po-Chen Lin, Seyyedali Hosseinalipour, Sheikh Shams Azam +2
Federated learning has emerged as a popular technique for distributing model training across the network edge. Its learning architecture is conventionally a star topology between t…
Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregations
Frank Po-Chen Lin, Seyyedali Hosseinalipour, Sheikh Shams Azam +2
Federated learning has emerged as a popular technique for distributing machine learning (ML) model training across the wireless edge. In this paper, we propose two timescale hybrid…
Can we Generalize and Distribute Private Representation Learning?
Sheikh Shams Azam, Taejin Kim, Seyyedali Hosseinalipour +3
We study the problem of learning representations that are private yet informative, i.e., provide information about intended "ally" targets while hiding sensitive "adversary" attrib…
Multi-Stage Hybrid Federated Learning over Large-Scale D2D-Enabled Fog Networks
Seyyedali Hosseinalipour, Sheikh Shams Azam, Christopher G. Brinton +4
Federated learning has generated significant interest, with nearly all works focused on a "star" topology where nodes/devices are each connected to a central server. We migrate awa…
Q-Map: Clinical Concept Mining from Clinical Documents
Sheikh Shams Azam, Manoj Raju, Venkatesh Pagidimarri +1
Over the past decade, there has been a steep rise in the data-driven analysis in major areas of medicine, such as clinical decision support system, survival analysis, patient simil…