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Antonious M. Girgis

2 accepted papers

2023

A Statistical Framework for Personalized Federated Learning and Estimation: Theory, Algorithms, and Privacy

ICLR 2023poster

A distinguishing characteristic of federated learning is that the (local) client data could have statistical heterogeneity. This heterogeneity has motivated the design of personalized learning, where individual (personalized) models are trained, through collaboration. There have been various persona…

Cited by 12SourcePDFScholar
2021

Renyi Differential Privacy of The Subsampled Shuffle Model In Distributed Learning

NeurIPS 2021poster

We study privacy in a distributed learning framework, where clients collaboratively build a learning model iteratively through interactions with a server from whom we need privacy. Motivated by stochastic optimization and the federated learning (FL) paradigm, we focus on the case where a small fract…

Cited by 0SourcePDFScholar