Search Results for author: Fan Mo

Found 9 papers, 2 papers with code

Centaur: Federated Learning for Constrained Edge Devices

no code implementations8 Nov 2022 Fan Mo, Mohammad Malekzadeh, Soumyajit Chatterjee, Fahim Kawsar, Akhil Mathur

Federated learning (FL) on deep neural networks facilitates new applications at the edge, especially for wearable and Internet-of-Thing devices.

Federated Learning

Machine Learning with Confidential Computing: A Systematization of Knowledge

no code implementations22 Aug 2022 Fan Mo, Zahra Tarkhani, Hamed Haddadi

Privacy and security challenges in Machine Learning (ML) have become increasingly severe, along with ML's pervasive development and the recent demonstration of large attack surfaces.

Towards Battery-Free Machine Learning and Inference in Underwater Environments

no code implementations16 Feb 2022 Yuchen Zhao, Sayed Saad Afzal, Waleed Akbar, Osvy Rodriguez, Fan Mo, David Boyle, Fadel Adib, Hamed Haddadi

This paper is motivated by a simple question: Can we design and build battery-free devices capable of machine learning and inference in underwater environments?

BIG-bench Machine Learning

Quantifying and Localizing Usable Information Leakage from Neural Network Gradients

no code implementations28 May 2021 Fan Mo, Anastasia Borovykh, Mohammad Malekzadeh, Soteris Demetriou, Deniz Gündüz, Hamed Haddadi

Our proposed framework enables clients to localize and quantify the private information leakage in a layer-wise manner, and enables a better understanding of the sources of information leakage in collaborative learning, which can be used by future studies to benchmark new attacks and defense mechanisms.

PPFL: Privacy-preserving Federated Learning with Trusted Execution Environments

1 code implementation29 Apr 2021 Fan Mo, Hamed Haddadi, Kleomenis Katevas, Eduard Marin, Diego Perino, Nicolas Kourtellis

We propose and implement a Privacy-preserving Federated Learning ($PPFL$) framework for mobile systems to limit privacy leakages in federated learning.

Federated Learning Privacy Preserving

Layer-wise Characterization of Latent Information Leakage in Federated Learning

no code implementations17 Oct 2020 Fan Mo, Anastasia Borovykh, Mohammad Malekzadeh, Hamed Haddadi, Soteris Demetriou

Training deep neural networks via federated learning allows clients to share, instead of the original data, only the model trained on their data.

Federated Learning

DarkneTZ: Towards Model Privacy at the Edge using Trusted Execution Environments

2 code implementations12 Apr 2020 Fan Mo, Ali Shahin Shamsabadi, Kleomenis Katevas, Soteris Demetriou, Ilias Leontiadis, Andrea Cavallaro, Hamed Haddadi

We present DarkneTZ, a framework that uses an edge device's Trusted Execution Environment (TEE) in conjunction with model partitioning to limit the attack surface against Deep Neural Networks (DNNs).

Image Classification

Towards Characterizing and Limiting Information Exposure in DNN Layers

no code implementations13 Jul 2019 Fan Mo, Ali Shahin Shamsabadi, Kleomenis Katevas, Andrea Cavallaro, Hamed Haddadi

Pre-trained Deep Neural Network (DNN) models are increasingly used in smartphones and other user devices to enable prediction services, leading to potential disclosures of (sensitive) information from training data captured inside these models.

Weight-importance sparse training in keyword spotting

no code implementations2 Jul 2018 Sihao Xue, Zhenyi Ying, Fan Mo, Min Wang, Jue Sun

Besides this, at most of time, ASR system is used to deal with real-time problem such as keyword spotting (KWS).

Keyword Spotting speech-recognition +1

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