Picture of Rawad Bitar

Dr. Ph.D. Rawad Bitar

Technical University of Munich

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

Postal address

Theresienstr. 90
80333 München


Senior researcher and lecturer doing a habilitation with Prof. Dr.-Ing Antonia Wachter-Zeh. I obtained a PhD from the ECE department of Rutgers University in January 2020. During my PhD, I held short term visiting positions at Aalto, Technical University of Berlin and the Chinese University of Hong Kong. In addition, I spent three years as PhD candidate at the ECE department of Illinois Institute of Technology (IIT). From August 2014 till January 2020, I was a member of the CSI lab supervised by Prof. Salim El Rouayheb.

In terms of previous studies, I received a masters degree in Information and Communication from the Lebanese University after doing the thesis at IIT in 2014. I graduated as a Computer and Communication Engineer from the Lebanese University in 2013 after doing the engineering senior project at Center of Nuclear Science and Science of Matter (CSNSM) in Paris, France and  an internship at procsim-consulting company in EPFL, Lausanne, Switzerland, in 2012.

Research Interests

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)

  • 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


Available Theses

MAB-Based Efficient Distributed ML on the Cloud

Distributed Machine Learning (ML), Multi-Armed Bandits (MABs), Cloud Simulations (AWS, GCP, ...)


We consider the problem of running a distributed machine learning algorithm on the cloud. This imposes several challenges. In particular, cloud instances may have different performances/speeds. To fully leverage the performance of the instances, we want to characterize their speed and potentially use the fastest ones. To explore the speed of the instances while exploiting them (assigning computational tasks), we use the theory of multi-armed bandits (MABs).

The goal of the research intership is to start by implementing existing theoretical algorithms [1] and possibly adapting them based on the experimental observations.

[1] M. Egger, R. Bitar, A. Wachter-Zeh and D. Gündüz, Efficient Distributed Machine Learning via Combinatorial Multi-Armed Bandits, submitted to IEEE Journal on Selected Areas in Communications (JSAC), 2022.


  • Information Theory
  • Machine Learning Basics
  • Python (Intermediate Level)


Private and Secure Federated Learning


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.


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


Theses in Progress