Foto von Rawad Bitar

Dr. Ph.D. Rawad Bitar

Technische Universität München

Lehrstuhl für Nachrichtentechnik (Prof. Kramer)


Theresienstr. 90
80333 München




My research interest is centered around privacy, scalability, security and reliability of distributed systems. The applications may differ but the goal remains the same: study theoretical and fundamental limits of innovative storage and computing systems and design codes achieving those limits. My current work is motivated by the following research directions.

  • Federated learning
  • Private and secure distributed computing
  • Distributed storage
  • DNA-based storage systems
  • Private and secure network coding.


  • DFG (German Research Foundation) grant for a temporary position for a Principal Investigator "Private Secure and Efficient Codes for Distributed Machine Learning". (2023 -- 2026)
  • EuroTech Visiting Research Programme grant. (March 2023)
  • Best Poster Award on Optimization and Machine Learning in Princeton Day of Optimization, Princeton, New Jersey. (September 2018)


Workshop co-Organizer

  • Munich Workshop on Coding and Cryptography 2022

Guest Editor

  • Frontier in Communications and Networks

Technical Program Committee (TPC) member

  • Conference on Information-Theoretic Cryptography (ITC) 2023
  • IEEE International Workshop on Information Theory (ITW) 2023
  • 13th Annual Non-Volatile Memories Workshop (NVMW) 2022

Session Chair

  • IEEE Information Theory Workshop (ITW) 2022
  • IEEE International Symposium on Information Theory (ISIT) 2022
  • IEEE International Symposium on Information Theory (ISIT) 2021


  • IEEE Transactions on Information Theory
  • IEEE Journal on Selected Areas in Information Theory (JSAIT)
  • IEEE Transactions on Forensics and Information Security
  • IEEE/ACM Transactions on Networking
  • IEEE Journal on Selected Areas in Communications (JSAC)
  • IEEE Transactions on Communications
  • IEEE Transactions on Parallel and Distributed Systems
  • IEEE Transactions on Emerging Topics in Computing
  • IEEE Transactions on Vehicular Technology
  • ScienceDirect Journal of Parallel and Distributed Computing
  • IET Information Security
  • IEEE International Symposium on Information Theory (ISIT)
  • IEEE Information Theory Workshop (ITW)
  • IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • IEEE Global Communication Conference: Communication Theory (Globecom)
  • IEEE Global Communication Conference: Workshops: Network Coding and Applications (NetCod)
  • IEEE Wireless Communications and Networking Conference (WCNC)


  • Winter semester 2021/2022: Security in Communications and Networks, jointly with Prof. Dr.-Ing Antonia Wachter-Zeh
  • Summer semester 2021: Coding Theory for Storage and Networks, jointly with Dr.-Ing Sven Puchinger
  • Winter semester 2020/2021: Security in Communications and Networks, jointly with Prof. Dr.-Ing Antonia Wachter-Zeh
  • Summer semester 2020: Coding Theory for Storage and Networks, jointly with Dr. Alessandro Neri


Angebotene Abschlussarbeiten

Laufende Abschlussarbeiten

Privacy-preserving synchronization methods


In this internship, the student will explore the literature on privacy-preserving synchronization methods and summarizes the findings.


Effect of Redundancy in Distributed Learning

Distributed Learning, Gradient Coding, Straggler tolerance


In this project, we investigate the interplay between redundancy and straggler tolerance in distributed learning.

The setting is that of a main node distributing computational tasks to available workers as part of a machine learning algorithm, e.g., training a neural network. Waiting for all workers to return their computations suffers from the presence of stragglers, i.e., slow or unresponsive nodes. Mitigating the effect of the stragglers can be done through the use of redundancy or by leveraging the properties of the convergence of the machine learning algorithm. 

The goal of this work is to compare when redundancy is helpful. In this case, we aim to analyze the convergence speed with and without redundancy. Then, we compare the convergence as a function of time of all the schemes.

Further reading: 

R. Bitar, M. Wootters and S. El Rouayheb, Stochastic Gradient Coding for Straggler Mitigation in Distributed Learning, IEEE Journal on Selected Areas in Information Theory (JSAIT), Vol. 1, No. 1, May 2020. arXiv:1905.05383

S. Kas Hanna, R. Bitar, P. Parag, V. Dasari and S. El Rouayheb, Adaptive Stochastic Gradient Descent for Fast and Communication-Efficient Distributed Learning, preprint, arXiv:2208.03134.


Knowledge in the following topics:

  • Probability Theory
  • Gradient descent and stochastic gradient descent
  • Coding theory

Independence and motivation to work on a research topic

Knowledge of implementing neural networks is a plus



Dr. Rawad Bitar: