Search Results for author: Heiko Ludwig

Found 12 papers, 3 papers with code

FLoRA: Single-shot Hyper-parameter Optimization for Federated Learning

no code implementations15 Dec 2021 Yi Zhou, Parikshit Ram, Theodoros Salonidis, Nathalie Baracaldo, Horst Samulowitz, Heiko Ludwig

We address the relatively unexplored problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO).

Federated Learning

FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data

no code implementations5 Mar 2021 Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar, James Joshi, Heiko Ludwig

We empirically demonstrate the applicability for multiple types of ML models and show a reduction of 10%-70% of training time and 80% to 90% in data transfer with respect to the state-of-the-art approaches.

Federated Learning Privacy Preserving

Adaptive Histogram-Based Gradient Boosted Trees for Federated Learning

no code implementations11 Dec 2020 Yuya Jeremy Ong, Yi Zhou, Nathalie Baracaldo, Heiko Ludwig

This approach makes the use of gradient boosted trees practical in enterprise federated learning.

Federated Learning

Mitigating Bias in Federated Learning

no code implementations4 Dec 2020 Annie Abay, Yi Zhou, Nathalie Baracaldo, Shashank Rajamoni, Ebube Chuba, Heiko Ludwig

As methods to create discrimination-aware models develop, they focus on centralized ML, leaving federated learning (FL) unexplored.

Fairness Federated Learning

TiFL: A Tier-based Federated Learning System

no code implementations25 Jan 2020 Zheng Chai, Ahsan Ali, Syed Zawad, Stacey Truex, Ali Anwar, Nathalie Baracaldo, Yi Zhou, Heiko Ludwig, Feng Yan, Yue Cheng

To this end, we propose TiFL, a Tier-based Federated Learning System, which divides clients into tiers based on their training performance and selects clients from the same tier in each training round to mitigate the straggler problem caused by heterogeneity in resource and data quantity.

Federated Learning

HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning

no code implementations12 Dec 2019 Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar, Heiko Ludwig

Participants in a federated learning process cooperatively train a model by exchanging model parameters instead of the actual training data, which they might want to keep private.

Federated Learning Privacy Preserving

Towards Federated Graph Learning for Collaborative Financial Crimes Detection

no code implementations19 Sep 2019 Toyotaro Suzumura, Yi Zhou, Natahalie Baracaldo, Guangnan Ye, Keith Houck, Ryo Kawahara, Ali Anwar, Lucia Larise Stavarache, Yuji Watanabe, Pablo Loyola, Daniel Klyashtorny, Heiko Ludwig, Kumar Bhaskaran

Advances in technology used in this domain, including machine learning based approaches, can improve upon the effectiveness of financial institutions' existing processes, however, a key challenge that most financial institutions continue to face is that they address financial crimes in isolation without any insight from other firms.

Federated Learning Graph Learning

Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering

1 code implementation9 Nov 2018 Bryant Chen, Wilka Carvalho, Nathalie Baracaldo, Heiko Ludwig, Benjamin Edwards, Taesung Lee, Ian Molloy, Biplav Srivastava

While machine learning (ML) models are being increasingly trusted to make decisions in different and varying areas, the safety of systems using such models has become an increasing concern.


Adversarial Robustness Toolbox v1.0.0

5 code implementations3 Jul 2018 Maria-Irina Nicolae, Mathieu Sinn, Minh Ngoc Tran, Beat Buesser, Ambrish Rawat, Martin Wistuba, Valentina Zantedeschi, Nathalie Baracaldo, Bryant Chen, Heiko Ludwig, Ian M. Molloy, Ben Edwards

Defending Machine Learning models involves certifying and verifying model robustness and model hardening with approaches such as pre-processing inputs, augmenting training data with adversarial samples, and leveraging runtime detection methods to flag any inputs that might have been modified by an adversary.

Adversarial Robustness BIG-bench Machine Learning +2

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