Foto von Marvin Xhemrishi

M.Sc. Marvin Xhemrishi

Technische Universität München

Professur für Codierung und Kryptographie (Prof. Wachter-Zeh)

Postadresse

Postal:
Theresienstr. 90
80333 München

Abschlussarbeiten

Angebotene Abschlussarbeiten

SwiftAgg Federated Learning

Beschreibung

Federated learning is a promising learning method for data that is decentralized. For privacy reasons, the devices that own the data cannot share it without performing extra computations (such as encryption, secret sharing etc). Since the end devices have to compute local gradients over their dataset, then the presence of low processing nodes, referred to as stragglers, can outweigh the benefits of the parallelism. A central entity serves as an aggregation node, that aggregates the local gradients received from the end devices. Due to privacy reasons, the aggregation node shall not learn the dataset of a device from the local gradient it computes. SwiftAgg[1] is a scheme that allows straggler resiliency and secure aggregation. 

 

[1] - https://arxiv.org/pdf/2202.04169.pdf

Voraussetzungen

- Information Theory

Kontakt

marvin.xhemrishi@tum.de

Betreuer:

Laufende Abschlussarbeiten

Publikationen

2022

  • Bitar, R.; Xhemrishi M.; Wachter-Zeh A.: Adaptive private distributed matrix multiplication. IEEE Transactions on Information Theory, 2022 mehr…
  • Garb K.; Xhemrishi M.; Kürzinger L.; Frisch C.: The Wiretap Channel for Capacitive PUF-Based Security Enclosures. IACR Transactions on Cryptographic Hardware and Embedded Systems, 2022 mehr…
  • Maringer G.; Xhemrishi M.; Puchinger S.; Garb K.; Liu H.; Jerkovits T.; Kürzinger L.; Hiller M.; Wachter-Zeh A.: Analysis of Communication Channels Related to Physical Unclonable Functions. Workshop on Coding and Cryptography (WCC), 2022 mehr…
  • Xhemrishi M.: Distributed Linear Computations over Private Sparse Matrices. IEEE European School of Information Theory, 2022 mehr…
  • Xhemrishi M.: Distributed Linear Computations over Private Sparse Matrices. Munich Workshop on Coding and Cryptography, 2022 mehr…
  • Xhemrishi M.: Computational Code-Based Privacy for Coded Federated Learning. TUM ICE Workshop Raitenhaslach, 2022 mehr…
  • Xhemrishi M.; Bitar R.; Wachter-Zeh A.: Distributed Matrix-Vector Multiplication with Sparsity and Privacy Guarantees. IEEE International Symposium on Information Theory, 2022 mehr…
  • Xhemrishi M.; Egger M. Bitar R.: Efficient Private Storage of Sparse Machine Learning Data. IEEE Information Theory Workshop, 2022 mehr…
  • Xhemrishi M.; Graell i Amat A.; Rosnes E.; Wachter-Zeh A.: Computational Code-Based Privacy in Coded Federated Learning. IEEE International Symposium on Information Theory, 2022 mehr…

2021

  • Maringer G.; Xhemrishi M.; Puchinger S.; Garb K.; Liu H.; Jerkovits T.; Hiller M.; Wachter-Zeh A.Kürzinger L.;: Analysis of Communication Channels related to Physically Unclonable Functions. arXiv, 2021 mehr…
  • Xhemrishi M.: Trade-off between privacy and sparsity in coded computing. 36th meeting of ITG Professional Group "Applied Information Theory", 2021 mehr…

2020

  • Bitar R.; Xhemrishi M.; Wachter-Zeh A.: Rateless Private Matrix-Matrix Multiplication. European School of Information Theory, 2020 mehr…
  • Bitar R.; Xhemrishi M.; Wachter-Zeh A.: Fountain codes for Private Distributed Matrix-Matrix Multiplication. International Symposium in Information Theory and its Applications, 2020 mehr…

2019

  • Xhemrishi, M.; Coşkun, M. C.; Liva, G.; Östman, J.; Durisi, G. : List Decoding of Short Codes for Communication over Unknown Fading Channels. , Workshop on Coding, Cooperation, and Security in Modern Communication Networks (COCO 2019) . , 2019 mehr…
  • Xhemrishi, M.; Coşkun, M. C.; Liva, G.: List Decoding for Fading Channels. Oberpfaffenhofen Workshop on High Throughput Coding (OWHTC) , 2019 mehr…
  • Xhemrishi, M.; Coşkun, M. C.; Liva, G.; Östman, J.; Durisi, G.: List Decoding of Short Codes for Communication over Unknown Fading Channels. Asilomar Conference on Signals, Systems, and Computers, 2019 mehr…