Farhad Pourpanah — accepted AI-conference papers

2025

Federated Domain Generalization with Label Smoothing and Balanced Decentralized Training

Milad Soltany, Farhad Pourpanah, Mahdiyar Molahasani, Michael A. Greenspan, Ali Etemad

ICASSP 2025accepted

In this paper, we propose a novel approach, Federated Domain Generalization with Label Smoothing and Balanced Decentralized Training (FedSB), to address the challenges of data heterogeneity within a federated learning framework. FedSB utilizes label smoothing at the client level to prevent overfitti…

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2025

Federated Unsupervised Domain Generalization Using Global and Local Alignment of Gradients

Farhad Pourpanah, Mahdiyar Molahasani, Milad Soltany, Michael Greenspan, Ali Etemad

AAAI 2025technical

We address the problem of federated domain generalization in an unsupervised setting for the first time. We first theoretically establish a connection between domain shift and alignment of gradients in unsupervised federated learning and show that aligning the gradients at both client and server lev…

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