Picture of Maximilian Egger

M.Sc. Maximilian Egger

Technical University of Munich

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

Postal address

Postal:
Theresienstr. 90
80333 München

Biography

Maximilian Egger received the B.Eng. in Electrical Engineering from the University of Applied Sciences Augsburg in 2020, and the M.Sc. in Electrical Engineering and Information Technology from the Technical University of Munich in 2022, both with high distinction (final grades: 1.0). He pursued a dual bachelor study accompanied by engineering positions in different hardware and software development departments at the Hilti AG. Inspiring collaborations at university, industry and the German Academic Scholarship Foundation strengthened his motivation to drive scientific progress. As a doctoral researcher at the Institute for Communications Engineering under the supervision of Prof. Dr.-Ing. Antonia Wachter-Zeh, he is conducting research in the rapidly growing field of large-scale decentralized computing and federated learning. Sensible data, potentially corrupted computations and stochastic environments naturally lead to concerns about privacy, security and efficiency. As part of his research, he investigates these problems from a coding and information-theoretic perspective.

 

Teaching

Coding Theory for Storage and Networks [Summer Term 2022]
Fast Secure and Reliable Coded Computing [Winter Term 2022/23]
Nachrichtentechnik [Summer Term 2023]

Theses

Available Theses

Theses in Progress

Secure Decentralized Learning with Gradient Compression

Description

We investigate the security of decentralized learning with gradient compression via Zero-order methods.

Supervisor:

Reliable Over-the-Air Computation for Federated Learning

Description

We study the usage of channel codes for the setting of federated learning with over-the-air computation so that the sum of codewords over reals can be efficiently and reliably decoded at the federator to obtain the average of partial model updates.

Supervisor:

Publications

2023

  • Egger, Maximilian; Hofmeister, Christoph; Wachter-Zeh, Antonia; Bitar, Rawad: Private Aggregation in Wireless Federated Learning with Heterogeneous Clusters. 2023 IEEE International Symposium on Information Theory (ISIT), IEEE, 2023 more… Full text ( DOI )
  • Maximilian Egger, Christoph Hofmeister, Antonia Wachter-Zeh, Rawad Bitar: Private Aggregation in Wireless Federated Learning with Heterogeneous Clusters. International Symposium on Information Theory, 2023 more…
  • Maximilian Egger, Marvin Xhemrishi, Antonia Wachter-Zeh, Rawad Bitar: Sparse and Private Distributed Matrix Multiplication with Straggler Tolerance. International Symposium on Information Theory, 2023 more…
  • Maximilian Egger, Rawad Bitar, Antonia Wachter-Zeh, Deniz Gündüz, Nir Weinberger: Maximal-Capacity Discrete Memoryless Channel Identification. International Symposium on Information Theory, 2023 more…
  • Maximilian Egger, Serge Kas Hanna, Rawad Bitar: Fast and Straggler-Tolerant Distributed SGD with Reduced Computation Load. International Symposium on Information Theory, 2023 more…
  • Thomas Schamberger, Maximilian Egger, Lars Tebelmann: Hide and Seek: Using Occlusion Techniques for Side-Channel Leakage Attribution in CNNs. Artificial Intelligence in Hardware Security Workshop, 2023 more…

2022

  • Marvin Xhemrishi, Maximilian Egger, Rawad Bitar: Efficient Private Storage of Sparse Machine Learning Data. 2022 IEEE Information Theory Workshop (ITW), 2022 more…
  • Maximilian Egger: Challenges in Federated Learning - A Brief Overview. TUM ICE Workshop Raitenhaslach, 2022 more…
  • Maximilian Egger, Rawad Bitar, Antonia Wachter-Zeh, Deniz Gündüz: Efficient Distributed Machine Learning via Combinatorial Multi-Armed Bandits. 2022 IEEE International Symposium on Information Theory (ISIT), 2022 more…
  • Maximilian Egger, Rawad Bitar, Antonia Wachter-Zeh, Deniz Gündüz: Cost-Efficient Distributed Learning via Combinatorial Multi-Armed Bandits. 2022 Munich Workshop on Coding and Cryptography (MWCC), 2022 more…
  • Maximilian Egger, Rawad Bitar, Antonia Wachter-Zeh, Deniz Gündüz: Cost-Efficient Distributed Learning via Combinatorial Multi-Armed Bandits. 2022 IEEE European School of Information Theory (ESIT), 2022 more…
  • Maximilian Egger, Thomas Schamberger, Lars Tebelmann, Florian Lippert, Georg Sigl: A Second Look at the ASCAD Databases. 14th International Workshop on Constructive Side-Channel Analysis and Secure Design, 2022 more…
  • Xhemrishi M.; Egger M. Bitar R.: Efficient Private Storage of Sparse Machine Learning Data. IEEE Information Theory Workshop, 2022 more…