Search Results for author: Joseph Soriaga

Found 8 papers, 0 papers with code

FP8 versus INT8 for efficient deep learning inference

no code implementations31 Mar 2023 Mart van Baalen, Andrey Kuzmin, Suparna S Nair, Yuwei Ren, Eric Mahurin, Chirag Patel, Sundar Subramanian, Sanghyuk Lee, Markus Nagel, Joseph Soriaga, Tijmen Blankevoort

We theoretically show the difference between the INT and FP formats for neural networks and present a plethora of post-training quantization and quantization-aware-training results to show how this theory translates to practice.

Quantization

An Expectation-Maximization Perspective on Federated Learning

no code implementations19 Nov 2021 Christos Louizos, Matthias Reisser, Joseph Soriaga, Max Welling

Federated learning describes the distributed training of models across multiple clients while keeping the data private on-device.

Federated Learning

Unsupervised Information Obfuscation for Split Inference of Neural Networks

no code implementations23 Apr 2021 Mohammad Samragh, Hossein Hosseini, Aleksei Triastcyn, Kambiz Azarian, Joseph Soriaga, Farinaz Koushanfar

In our method, the edge device runs the model up to a split layer determined based on its computational capacity.

Federated Learning of User Verification Models Without Sharing Embeddings

no code implementations18 Apr 2021 Hossein Hosseini, Hyunsin Park, Sungrack Yun, Christos Louizos, Joseph Soriaga, Max Welling

We consider the problem of training User Verification (UV) models in federated setting, where each user has access to the data of only one class and user embeddings cannot be shared with the server or other users.

Federated Learning

Secure Federated Learning of User Verification Models

no code implementations1 Jan 2021 Hossein Hosseini, Hyunsin Park, Sungrack Yun, Christos Louizos, Joseph Soriaga, Max Welling

We consider the problem of training User Verification (UV) models in federated setup, where the conventional loss functions are not applicable due to the constraints that each user has access to the data of only one class and user embeddings cannot be shared with the server or other users.

Federated Learning

Federated Averaging as Expectation Maximization

no code implementations1 Jan 2021 Christos Louizos, Matthias Reisser, Joseph Soriaga, Max Welling

Federated averaging (FedAvg), despite its simplicity, has been the main approach in training neural networks in the federated learning setting.

Federated Learning

Private Split Inference of Deep Networks

no code implementations1 Jan 2021 Mohammad Samragh, Hossein Hosseini, Kambiz Azarian, Joseph Soriaga

Splitting network computations between the edge device and the cloud server is a promising approach for enabling low edge-compute and private inference of neural networks.

Federated Learning of User Authentication Models

no code implementations9 Jul 2020 Hossein Hosseini, Sungrack Yun, Hyunsin Park, Christos Louizos, Joseph Soriaga, Max Welling

In this paper, we propose Federated User Authentication (FedUA), a framework for privacy-preserving training of UA models.

Federated Learning Privacy Preserving +1

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