Picture of Christoph Hofmeister

M.Sc. Christoph Hofmeister

Technical University of Munich

Associate Professorship of Coding and Cryptography (Prof. Wachter-Zeh)

Postal address

Postal:
Theresienstr. 90
80333 München

Biography

  • Dual Study Program with Infineon Technologies (2015-2019)
  • B.Eng. in Electrical and Computer Engineering, University of Applied Sciences Munich (2019)
  • M.Sc. in Electrical and Computer Engineering, Technical University of Munich (2021)
  • Since October 2021, doctoral researcher at the Institute of Communications Engineering, Coding and Cryptography group

Theses

Available Theses

Private and Secure Federated Learning

Description

In federated learning, a machine learning model shall be trained on private user data with the help of a central server, the so-called federator. This setting differs from other machine learning settings in that the user data shall not be shared with the federator for privacy reasons and/or to decrease the communication load of the system.

Even though only intermediate results are shared, extra care is necessary to guarantee data privacy. An additional challenge arises if the system includes malicious users that breach protocol and send corrupt computation results.

The goal of this work is to design, implement and analyze coding- and information-theoretic solutions for privacy and security in federated learning.

Prerequisites

  • Coding Theory (e.g., Channel Coding)
  • Information Theory
  • Machine Learning Basics

Supervisor:

Theses in Progress

A Framework for Federated Learning with Variable Local Updates

Description

Since the introduction of federated learning in [1], we can observe a rapidly growing body of research. In particular, we face challenges with respect to privacy, security and efficiency. We build upon an existing generic framework for simulating decentralized optimization procedures in a federated learning setting. With the help of that framework, the student should analyze the performance of selected state-of-the-art schemes and investigate different protocols that utilize local updates and the effect of straggling clients.

Supervisor:

Publications

2022

  • Christoph Hofmeister; Luis Maßny; Rawad Bitar; Eitan Yaakobi: Trading Communication and Computation for Security in Gradient Coding. Munich Workshop on Coding and Cryptography 2022, 2022 more…
  • Christoph Hofmeister; Luis Maßny; Rawad Bitar; Eitan Yaakobi: Trading Communication and Computation for Security in Gradient Coding. 2022 IEEE European School of Information Theory (ESIT), 2022 more…
  • Hofmeister Christoph; Luis Maßny; Rawad Bitar; Eitan Yaakobi: Trading Communication and Computation for Security in Gradient Coding. TUM ICE Workshop Raitenhaslach, 2022 more…
  • Hofmeister, Christoph; Bitar, Rawad; Xhemrishi, Marvin; Wachter-Zeh, Antonia: Secure Private and Adaptive Matrix Multiplication Beyond the Singleton Bound. IEEE Journal on Selected Areas in Information Theory 3 (2), 2022, 275-285 more… Full text ( DOI )
  • Hofmeister, Christoph; Bitar, Rawad; Xhemrishi, Marvin; Wachter-Zeh, Antonia: Secure Private and Adaptive Matrix Multiplication Beyond the Singleton Bound. WCC 2022: The Twelfth International Workshop on Coding and Cryptography , 2022 more…