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Martin Pelikan

2 accepted papers

2025

Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers, and Gradient Clipping

NeurIPS 2025poster

While federated learning (FL) and differential privacy (DP) have been extensively studied, their application to automatic speech recognition (ASR) remains largely unexplored due to the challenges in training large transformer models. Specifically, large models further exacerbate issues in FL as they…

Cited by 0SourcecodeScholar
2024

$\texttt{pfl-research}$: simulation framework for accelerating research in Private Federated Learning

NeurIPS 2024poster

Federated learning (FL) is an emerging machine learning (ML) training paradigm where clients own their data and collaborate to train a global model, without revealing any data to the server and other participants. Researchers commonly perform experiments in a simulation environment to quickly iterat…

Cited by 2SourcePDFScholar