Postdoctoral researcher working 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 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.
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
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  and possibly adapting them based on the experimental observations.
 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.
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.
In this project, we implement distributed coordinate gradient descent in the master/worker setting on a local machine. The goal is to analyze the performance of this algorithm in the presence of stregglers.
The new idea here is to distribute the data redundantly to the workers so that stragglers do not affect the performance of the overall algorithm. We try a new scoring strategy of the coordinates that will reflect the repetition of each coordinate within the workers to guarantee that the master observes the important coordinates at each iteration with high probability.
We test our ideas on MNIST data set and potentially using CIFAR-10.
Processing a massive amount of collected data by means of machine learning algorithms often becomes infeasible when carried out on single machines. To cope with the computational requirements, distributed cloud computing was introduced. Thereby, a large computational task is split into multiple parts and distributed among worker machines to parallelize the computations and thereby speed up the learning process. However, since confidential data must be shared with third parties and the outcome is threatened by potential corrupt computations, privacy and security has to be ensured. This is particularly critical in medical environments, in which we deal with individual patients' information.
To motivate the study of these challenges, a competition called iDash privacy and security workshop is hosted every year . This year, the task is to develop a framework that securely links similar patient related entries being stored on different datasets without comprising privacy - for example to avoid double considerations in further processing steps. During this research internship, the student should use multi-party computation tools to develop a framework that complies with the aforementioned requirements.