Search Results for author: Thomas Schneider

Found 18 papers, 2 papers with code

Long-term Large-scale Mapping and Localization Using maplab

1 code implementation28 May 2018 Marcin Dymczyk, Marius Fehr, Thomas Schneider, Roland Siegwart

This paper discusses a large-scale and long-term mapping and localization scenario using the maplab open-source framework.

maplab: An Open Framework for Research in Visual-inertial Mapping and Localization

1 code implementation28 Nov 2017 Thomas Schneider, Marcin Dymczyk, Marius Fehr, Kevin Egger, Simon Lynen, Igor Gilitschenski, Roland Siegwart

On the other hand, maplab provides the research community with a collection of multisession mapping tools that include map merging, visual-inertial batch optimization, and loop closure.

Robotics

Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications

no code implementations10 Jan 2018 M. Sadegh Riazi, Christian Weinert, Oleksandr Tkachenko, Ebrahim. M. Songhori, Thomas Schneider, Farinaz Koushanfar

Chameleon departs from the common assumption of additive or linear secret sharing models where three or more parties need to communicate in the online phase: the framework allows two parties with private inputs to communicate in the online phase under the assumption of a third node generating correlated randomness in an offline phase.

BIG-bench Machine Learning

Topomap: Topological Mapping and Navigation Based on Visual SLAM Maps

no code implementations16 Sep 2017 Fabian Blöchliger, Marius Fehr, Marcin Dymczyk, Thomas Schneider, Roland Siegwart

Then, we create a set of convex free-space clusters, which are the vertices of the topological map.

Robotics

CryptoSPN: Privacy-preserving Sum-Product Network Inference

no code implementations3 Feb 2020 Amos Treiber, Alejandro Molina, Christian Weinert, Thomas Schneider, Kristian Kersting

AI algorithms, and machine learning (ML) techniques in particular, are increasingly important to individuals' lives, but have caused a range of privacy concerns addressed by, e. g., the European GDPR.

Privacy Preserving

BAFFLE: TOWARDS RESOLVING FEDERATED LEARNING’S DILEMMA - THWARTING BACKDOOR AND INFERENCE ATTACKS

no code implementations1 Jan 2021 Thien Duc Nguyen, Phillip Rieger, Hossein Yalame, Helen Möllering, Hossein Fereidooni, Samuel Marchal, Markus Miettinen, Azalia Mirhoseini, Ahmad-Reza Sadeghi, Thomas Schneider, Shaza Zeitouni

Recently, federated learning (FL) has been subject to both security and privacy attacks posing a dilemmatic challenge on the underlying algorithmic designs: On the one hand, FL is shown to be vulnerable to backdoor attacks that stealthily manipulate the global model output using malicious model updates, and on the other hand, FL is shown vulnerable to inference attacks by a malicious aggregator inferring information about clients’ data from their model updates.

Federated Learning Image Classification

Conservative Extensions in Horn Description Logics with Inverse Roles

no code implementations19 Nov 2020 Jean Christoph Jung, Carsten Lutz, Mauricio Martel, Thomas Schneider

We investigate the decidability and computational complexity of conservative extensions and the related notions of inseparability and entailment in Horn description logics (DLs) with inverse roles.

Classification of Finite Highly Regular Vertex-Coloured Graphs

no code implementations2 Dec 2020 Irene Heinrich, Thomas Schneider, Pascal Schweitzer

A coloured graph is k-ultrahomogeneous if every isomorphism between two induced subgraphs of order at most k extends to an automorphism.

Combinatorics 05C60, 05B20, 05B05, 05C75

The agnostic sampling transceiver

no code implementations5 Aug 2020 Arijit Misra, Janosch Meier, Karanveer Singh, Stefan Preussler, Thomas Schneider

Increasing capacity demands in the access networks require inventive concepts for the transmission and distribution of digital as well as analog signals over the same network.

Optical Channel Aggregation by Coherent Spectral Superposition with Electro-Optic Modulators

no code implementations3 Sep 2021 Arijit Misra, Stefan Preussler, Karanveer Singh, Janosch Meier, Thomas Schneider

As the bit rates of routed data streams exceed the throughput of single wavelength-division multiplexing channels, spectral traffic aggregation becomes essential for optical network scaling.

Reconfigurable and Real-Time Nyquist OTDM Demultiplexing in Silicon Photonics

no code implementations19 Oct 2021 Arijit Misra, Karanveer Singh, Janosch Meier, Christian Kress, Tobias Schwabe, Stefan Preußler, J. Christoph Scheytt, Thomas Schneider

We demonstrate for the first time, to the best of our knowledge, reconfigurable and real-time orthogonal time-domain demultiplexing of coherent multilevel Nyquist signals in silicon photonics.

ScionFL: Efficient and Robust Secure Quantized Aggregation

no code implementations13 Oct 2022 Yaniv Ben-Itzhak, Helen Möllering, Benny Pinkas, Thomas Schneider, Ajith Suresh, Oleksandr Tkachenko, Shay Vargaftik, Christian Weinert, Hossein Yalame, Avishay Yanai

In this paper, we unite both research directions by introducing ScionFL, the first secure aggregation framework for FL that operates efficiently on quantized inputs and simultaneously provides robustness against malicious clients.

Federated Learning Quantization

HyFL: A Hybrid Framework For Private Federated Learning

no code implementations20 Feb 2023 Felix Marx, Thomas Schneider, Ajith Suresh, Tobias Wehrle, Christian Weinert, Hossein Yalame

Federated learning (FL) has emerged as an efficient approach for large-scale distributed machine learning, ensuring data privacy by keeping training data on client devices.

Data Poisoning Federated Learning +1

Orthogonal Sampling based Broad-Band Signal Generation with Low-Bandwidth Electronics

no code implementations8 Jun 2023 Mohamed I. Hosni, Janosch Meier, Younus Mandalawi, Karanveer Singh, Paulomi Mandal, Ahmed H. Elghandour, Thomas Schneider

To circumvent the sampling rate and bandwidth problem of electronic DACs, we demonstrate the generation of wide-band signals with low-bandwidth electronics.

Attesting Distributional Properties of Training Data for Machine Learning

no code implementations18 Aug 2023 Vasisht Duddu, Anudeep Das, Nora Khayata, Hossein Yalame, Thomas Schneider, N. Asokan

The success of machine learning (ML) has been accompanied by increased concerns about its trustworthiness.

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