Offene Abschlussarbeiten

In the realm of space communication, where delays and disruptions are inherent challenges, the efficacy of systems hinges on their ability to withstand such adversities. The adoption of Delay-/Distruption-Tolerant Networking (DTN) with the Bundle Protocol (v7) strives to facilitate communication and traffic flow in space despite intermittent disruptions. However, ensuring the resilience and efficiency of such systems requires more than just robust protocols; it demands access to realistic data and information for their design, implementation, and evaluation.

Researchers and developers working in this domain face a crucial problem: the scarcity of realistic data for simulations and testing. While real-world data exists, it often comes shrouded in non-disclosure agreements (NDAs), limiting its accessibility and adaptability. Furthermore, such data might lack the variability necessary to explore diverse and new scenarios, or adapt to different setups and hardware configurations.

The synthetic generation of information, data, and packets based on real-world counterparts helps to overcome such issues. By replicating the behavior of various sensor modules and their interactions by sophisticated models, researchers can generate synthetic data that closely mirrors real-world scenarios. But crucially, this modeling approach must embrace modularity and adaptivity. Different sensor modules can be modeled individually, allowing for their modular combination to simulate diverse setups and traffic patterns. Furthermore, module behavior should be adaptable, allowing testing new hardware configurations or exploring alternative communication protocols. At its best, this approach enables researchers to simulate and evaluate a plethora of scenarios with realistic communication patterns and minimal resource requirements.

Within the scope of this thesis you will

  • Delve into interesting topics like synthetic data generation and space communication using protocols such as PUS A/B/C, CFDP and other Space Packets
  • Model traffic patterns based on a real-world space communication data analysis
  • Implement a modular and adaptable generator tool for space communication patterns based on your analysis (preferrable in Rust or Python)
  • Test your model in a DTN simulator or an actual BP implementation and its applicability in combination with the Bundle Protocol v7
  • Scientifically evaluate your models and generators by an in-depth comparison to real-world data in the simulated environment
  • Provide an elaborate, scientific report (written in LaTeX)

Your profile:

  • Motivated, independent working style
  • Interest in, e.g., satellite communication, network protocols, simulations, ad-hoc and disruption-tolerant communication
  • Experience in programming, e.g., Python, Rust, or other OOPLs, is beneficial

This thesis is conducted in collaboration with associates of ESA/ESOC in Darmstadt. A separate NDA regaring usage of space communication data may be required.

Supervisor: Pratyush Agnihotri
KOM-ID: KOM-M-0770
Link zur Ausschreibung

Distributed Stream Processing Systems (DSPS) allow real-time processing of data to fetch meaningful information. Big giants like Twitter, Facebook, and Alibaba rely on DSP for real-time data analytics. For example, click analytics, credit card fraud detection, etc. In DSPS, queries are usually long-running. They typically deal with a very high workload of millions or even billions of events per second. Under such scenarios, parallelism plays an important role in providing scalability for DSPS where either the input stream is partitioned into multiple processes or sub-streams or duplicated to be processed by operators and their instances. However, existing DSPS lacks the benchmarking of the parallel processing capability of operators under various workloads, event rates, and different queries.

Research Goals:

  • Literature study of benchmarking parallel stream processing.
  • Implementing Window and Pane-based parallelization strategy.
  • Evaluating the performance for various benchmarks
  • Displaying the performance evaluation result on the front end.
  • Possible DSPS to be used: Apache Flink
Supervisor: Pratyush Agnihotri
KOM-ID: KOM-M-0720
Link zur Ausschreibung

Motivation

 In Distributed Stream Processing Systems (DSPS), queries are usually long-running. They typically deal with a very high workload of million or even billion of events per second. Under such scenarios, parallelism plays an important role in providing scalability for DSPS. One of the core
decisions here is to determine the right parallelization degree to process a high load of events and queries while ensuring high throughput and low latency requirements. In this context, parallelization can be controlled by defining the degree of parallelism for each operator in the operator graph.
The main questions we want to investigate here are -
 

  •  What is the right parallelism degree for query processing? (what is the performance in terms of end-to-end latency for parallel stream processing? At what extend the model can be used - seen and unseen behaviour? What are the limitations of models? At which can be extended?)
  • How to parallelize the processing of DSPs operators? 

 Prerequisites

 There are no hard guidelines for this topic. However, it is preferable if you have:

  • Good programming skills in Java and Python
  • Good understanding of the concepts of machine learning
  • Understanding of the concept of communication networks, big data processing engines, e.g., Apache Flink, Kafka. 

 Reference Literature

  • Three steps is all you need: fast, accurate, automatic scaling decisions for distributed streaming dataflows.
  • A Comprehensive Survey on Parallelization and Elasticity in Stream Processing
  • Resource Management and Scheduling in Distributed Stream Processing Systems: A Taxonomy, Review, and Future Directions
  • One Model to Rule them All: Towards Zero-Shot Learning for Databases Benjamin
  • Apache Flink documentation: nightlies.apache.org/flink/flink-docs-master/docs/learn-flink/overview/ [https://nightlies.apache.org/flink/flink-docsmaster/docs/learn-flink/overview/] 

Contact:

I am looking for motivated students interested in working on cutting-edge technologies and IoT applications area. If you are interested in writing a Bachelor's or Master's Thesis or gaining experience in MAKI project as HiWi, please feel free to contact me. You can email me the following information:

  • Your CV or small text about your courses and prior experiences in this area.
  • The short note on your motivation about this topic and,
  • Transcript or TUCaN grade list.

Motivation

In der Forschungsgruppe Adaptive Communication Systems (ACS) beim Lehrstuhl Multimedia Communications (KOM) beschäftigen wir uns u.a. mit der Frage, wie sich Kommunikationssysteme an aktuelle Rahmenbedingungen im Netzwerk anpassen lassen. Die Ziele dieser Adaption reichen von der Verbesserung der Dienstgüte (Quality of Service) in Katastrophenszenarien bis hin zur Verbesserung der Nutzererfahrung (Quality of Experience) im alltäglichen Leben. Im Kern dieser Adaption lässt sich Machine Learning (ML) anwenden, beispielsweise um das Verhalten von Netzwerkknoten ohne manuelle Vorgaben dynamisch an neue Rahmenbedungen anpassen zu können.

Die Kombination von Reinforcement Learning (RL) und Deep Learning (DL) ermöglicht in letzter Zeit große Erfolge, insbesondere auch im Netzwerkbereich. Motiviert durch die Adaption von Kommunikationssystemen betrachten wir im Rahmen dieser Ausschreibung das Lernen innerhalb von Systemen mit mehreren Agenten (Multi-Agent Reinforcement Learning, MARL) unter Unsicherheit und kooperativen Zielen. In einer Thesis werden Teilaspekte dieser Problemstellung behandelt.

Mögliche Schwerpunkte

Das spezifische Thema der Thesis können wir gemeinsam und in Abhängigkeit von deinen Interessen definieren. Schreib mir dazu einfach eine E-Mail, in der du dich kurz vorstellst. Aktuell sind Themen mit den folgenden Schwerpunkten möglich:

Cooperation and Communication Generalization
Agenten müssen miteinander Kooperieren und Informationen austauschen, um eine gemeinsame Problemstellung zu lösen. Ansätze zur Verbesserung der Generalisierbarkeit des gelernten Verhaltens, z.B. Übertragbarkeit auf andere Netzwerktopologien.

Wenn dich davon etwas anspricht, können wir konkretere Themenvorschläge zu den Schwerpunkten besprechen. Alternativ kannst du auch eine eigene Problemstellung vorschlagen, die Überschneidungen mit diesen Bereichen aufweist. Beispielsweise eine für dich interessante netzwerktechnische Problemstellung oder ein (Video)Spiel, für welches du eine AI entwickeln möchtest. Kooperation und Kommunikation sind oft zentrale Aspekte von verteilten Systemen.

Anforderungen

Bachelorarbeit Masterarbeit
  • Interesse an ML und RL
  • Grundlagen Mathematik, formale Methoden, Stochastik
  • Sehr gute Programmierkenntnisse (+ Linux-Grundlagen)
  • Größtenteils selbstständige Arbeitsweise, eigene Ideen entwickeln und diskutieren
  • Vorerfahrung in Deep Learning, insbesondere praktisch mit PyTorch
  • Vorerfahrung in Reinforcement Learning oder Control (Theory)
  • Siehe Anforderungen für Bachelorarbeit

Literatur zum Einstieg

Allgemeine Literatur zu (Deep) Reinforcement Learning in Multiagentensystemen und Netzwerken:

[1] P. Hernandez-Leal, B. Kartal and M. E. Taylor, "A survey and critique of multiagent deep reinforcement learning," in Autonomous Agents and Multi-Agent Systems, vol. 33, no. 6, pp. 750-797, 2019.
[2] N. C. Luong et al., “Applications of Deep Reinforcement Learning in Communications and Networking: A Survey,” IEEE Communications Surveys Tutorials, vol. 21, no. 4, Art. no. 4, 2019.

Zum Einstieg in RL ist Q-Learning im RL-Standardwerk [3] und Deep Q-Learning [4] zu empfehlen

[3] R. S. Sutton and A. G. Barto, Reinforcement learning: An introduction. MIT press, 2018.
[4] V. Mnih, et al, "Human-level control through deep reinforcement learning," in nature, vol. 518, no. 7540, pp. 529-533, 2015.

Zum Einstieg in Kommunikation ist TarMAC [5] empfehlenswert, eine Übersicht bietet das Survey von Zhu et al. [6]

[5] A. Das et al., “TarMAC: Targeted Multi-Agent Communication,” in Proceedings of the 36th International Conference on Machine Learning, vol. 97, pp. 1538–1546. [Online]. Available: http://proceedings.mlr.press/v97/das19a.html
[6] C. Zhu, M. Dastani, and S. Wang, “A Survey of Multi-Agent Reinforcement Learning with Communication.” arXiv, 2022. doi: 10.48550/ARXIV.2203.08975.

Supervisor: Pratyush Agnihotri
KOM-ID: KOM-M-0721
Link zur Ausschreibung

Motivation:

Internet of Things describes a new world of heterogeneous objects such as sensors, smartphones, actuators etc, which intelligently interacts and communicate with each other and offer a better quality of life. With the increase of these objects, the data volume is growing as well. Processing this volume of data can be helpful to various application domains to reveal complex patterns, gain fast insights or react to observed situations, e.g., fraud detection, smart factories, telecommunication, etc. However, it becomes extremely challenging to continuously process this high volume of data in a timely manner and meet these applications' demands. One of the possible ways can be to use heterogeneous resources' capability such as GPUs, FPGAs, etc. to accelerate the big data analytics instead of relying on homogeneous resources, e.g., CPU.

Task:

  • Analyze existing related literature.
  • Formalizing the problem and developing the solution and
  • Setting up development environment and implementing the proof of concept or prototype.
  • Evaluation of the proposed solution in comparison to the existing approaches. .

Prerequisites:

There are no hard guidelines for this topic. However, it is preferable if you have:

  • Good programming skills in Java and/or Scala.
  • Understanding of the concept of .
    • communication networks and .
    • big data processing engines, e.g., Apache Flink, Kafka.

Contact:

I am looking for motivated students interested in working on cutting-edge technologies and IoT applications area. If you are interested in writing a Bachelor's or Master's Thesis or gaining experience in MAKI project as HiWi, please feel free to contact me. You can email me the following information:

  • Your CV or small text about your courses and prior experiences in this area.
  • The short note on your motivation about this topic and,
  • Transcript or TUCaN grade list.

Abschlussarbeiten in Bearbeitung

Supervisor: Benjamin Becker Julian Zobel
KOM-ID: KOM-M-0783 Student: Paul Frommelt
Link zur Ausschreibung
Supervisor: Julian Zobel
KOM-ID: KOM-B-0722 Student: Ihsen Bouallegue
Link zur Ausschreibung
Supervisor: Julian Zobel
KOM-ID: KOM-B-0723 Student: Enrico Chies
Link zur Ausschreibung
Supervisor: Jannis Weil
KOM-ID: KOM-M-0782 Student: Zhenghua Bao
Link zur Ausschreibung

In dieser Arbeit werden Methoden untersucht, um Ansätze aus dem Bereich Deep Reinforcement Learning auf (Multi-Agent) Environments mit dynamischen (sich ändernden) Graphen anwenden zu können. Dies ist ein wichtiger Schritt zur Anwendung in realen Kommunikationsnetzwerken. Existierende Arbeiten sind hinsichtlich der möglichen Anpassungen im Graphen oftmals sehr beschränkt und/oder müssen bei starken Änderungen im Graphen neu trainiert werden.

Supervisor: Lisa Wernet
KOM-ID: KOM-M-0781 Student: Lukas Wehrstein
Link zur Ausschreibung
Supervisor: Lisa Wernet
KOM-ID: KOM-M-0780 Student: Fabian Dreßler
Link zur Ausschreibung
Supervisor: Lisa Wernet
KOM-ID: KOM-B-0721 Student: Mateusz Pawlowski
Link zur Ausschreibung

Die Data Plane eines 5G Zugangsnetzwerkes besteht aus mehreren Netzfunktionen, die über dedizierte Interfaces verbunden sind. Ziel dieser Arbeit ist es eine Leistungsanalyse entlang der Data Plane Netzfunktionen durchzuführen und zu evaluieren, welchen Einfluss einzelne Netzfunktionen auf die Ende-zu-Ende Performanz haben. Zusätzlich soll evaluiert werden, an welchen Stellen entlang der Data Plane welche Leistungsbeschränkungen existieren und mit welchen Mechanismen man die erkannten Verluste minimieren kann. Anschließend sollen diese Mechanismen in ausgewählten Netzfunktionen im Testnetzwerk des FG KOM implementiert werden und die Auswirkungen evaluiert werden.

Supervisor: Jannis Weil
KOM-ID: KOM-B-0720 Student: Ismail Yigit Toparlak
Link zur Ausschreibung

The overarching goal of this bachelor's thesis is to find out if approaches from the domain of learnable communication can enable collaborative perception in the Pommerman domain. Existing approaches for the team radio mode with partial observability employ heuristics to exchange information between agents in order to increase their win rate. As the communication bandwidth is very limited (only 6 bits per timestep), manually designing a sophisticated communication protocol is complex. Consequently, existing heuristics are kept simple and usually convey very litte information (e.g. own position and position of one opponent). However, both agents have access to further local information that could be worth sharing (e.g. powerup locations, map tiles, bomb locations and movement..). This thesis investigates if approaches with learnable communication could be used to create communication protocols that allow to convey more information under the same bandwidth constraints.

Supervisor: Tobias Meuser
KOM-ID: KOM-M-0779 Student: Dean Grove
Link zur Ausschreibung
Supervisor: Pratyush Agnihotri
KOM-ID: KOM-M-0778 Student: Jeffrey Resnik
Link zur Ausschreibung

To deal with rapidly changing workloads,  Distributed Stream Processing Systems rely on parallelism to use multiple instances of operators to process a high amount of data. In recent times, several heuristic-based and learned-based approaches have been proposed to tune parallelism, but they are limited to specific workloads and can't work for unseen workloads which could result in degraded performance and inefficient resource utilization. In this thesis, we look at how can end-to-end learning model be  used for parallelism prediction in distributed systems.  The proposed model should aim to accurately predict optimal parallelism levels under a variety of scenarios, thus improving data processing efficiency and resource utilization in distributed systems. 

Supervisor: Pratyush Agnihotri
KOM-ID: KOM-M-0777 Student: Mizuki Hashimoto
Link zur Ausschreibung

Graph Neural Networks (GNNs) have shown potential in various domains, including databases and distributed stream processing systems. However, the effectiveness and accuracy of GNN models, particularly in zero-shot learning scenarios, heavily depend on selecting appropriate features. This thesis aims to develop and evaluate feature selection strategies and an optimized method to select from feature selection strategies based on constraints that enhance the performance of zero-shot-based GNN models in the context of parallel stream processing.

Key Objectives

  • Identify and analyze the key features that impact the performance of GNN models in parallel stream processing tasks.
  • Develop a systematic feature selection strategy that optimizes the zero-shot learning capabilities of GNN models in distributed stream processing environments.
  • Evaluate the effectiveness of the proposed feature selection strategy in improving the accuracy, efficiency, and scalability of zero-shot GNN models.
Supervisor: Lisa Wernet
KOM-ID: KOM-B-0719 Student: Silas Gerock
Link zur Ausschreibung
Supervisor: Pegah Golchin
KOM-ID: KOM-M-0775 Student: Hengyu Liu
Link zur Ausschreibung
Supervisor: Ahmad Khalil
KOM-ID: KOM-M-0774 Student: Hani Aldebes
Link zur Ausschreibung

In recent years, there has been an increasing interest in adopting federated learning to train deep learning models. 
This heightened interest is primarily driven by the agility, data privacy, scalability, and efficiency that federated learning has demonstrated.
Federated learning (FL) has gained significant traction, particularly in developing and updating object detection models.
These models demand continuous refinement, a task that FL can efficiently accomplish. Nonetheless, a critical challenge for these models revolves around the collection and preparation of training data.
This issue is particularly pronounced in vehicular networks, where each vehicle or node must gather images and prepare them for their respective local models.
However, a pivotal prerequisite is labeling these images before they can be utilized in training supervised models.
This thesis presents several methodologies for image labeling, offering an in-depth exploration of each approach, and showing their respective advantages and drawbacks (e.g., in the model performance, required computational power, and network load).
Furthermore, it conducts a comprehensive assessment of the performance and accuracy of these approaches, utilizing a variety of metrics for evaluation.

Task

  • Explore federated learning applications, with a particular focus on their utilization within the domain of vehicular applications. 
  • Extend the implementation of the pre-existing framework for training federated learning-based object detection models. This extension should encompass the integration of diverse data labeling methodologies.
  • Conduct a thorough and exhaustive evaluation of the performance of these aforementioned approaches. This assessment should encompass the utilization of a diverse array of metrics for evaluation purposes, ensuring a comprehensive understanding of their respective strengths and weaknesses.
  • Report the findings

Prerequisites

It is required to have:
•    Motivated and individual working style
•    Very good programming skills in Python.
•    Very good machine learning / Deep learning knowledge

Contact

I am looking for motivated students interested in working on cutting-edge technologies and vehicular applications area. If you are interested in writing a Bachelor's or Master's Thesis, please feel free to contact me at Ahmad Khalil (ahmad.khalil(at)kom.tu-darmstadt.de). You can email me the following information:
•    Your CV or small text about your courses and prior experiences in this area.

Supervisor: Pegah Golchin
KOM-ID: KOM-M-0773 Student: Kexin Wang
Link zur Ausschreibung
Supervisor: Fridolin Siegmund
KOM-ID: KOM-M-0779 Student: Xiaonan Chen
Link zur Ausschreibung

Das am Fachgebiet KOM entwickelte OpenSource Framework P4STA (https://github.com/ralfkundel/P4STA) erlaubt hochgenaue Latenzmessungen mit Nanosekundengenauigkeit bei Bandbreiten von bis zu 100 Gbit/s. Um dies zu erreichen werden softwarebasierte Standardlastgeneratoren mit einem programmierbaren Netzwerkswitch kombiniert.

Nachteil hierbei ist, dass die Softwarelastgeneratoren zusätzliche Server benötigen und nur eingeschränkt flexibel sind. Spezielle Netzwerkprotokolle wie beispielsweise PPPoE oder GTP, was in 5G Netzwerken zum Einsatz kommt, können nur schwierig erzeugt werden.

Ziel dieser Arbeit ist es, dass P4STA framework um integrierte Paketgenerierung innerhalb des programmierbaren P4-Switches zu erweitern.
Der Vorteil dieser hardwarebasierten Lastgenerierung liegt zum einen in den hohen Datenraten; pro Port können so bis zu 100 Gbit/s erzeugt werden.
Außerdem können beliebige Protokollstacks erzeugt werden.

Voraussetzungen:

- Gutes Verständnis von Netzwerken und Protokollen
- Vorlesung Software Defined Networking (oder vergleichbar)
- idealerweise Grundkenntnisse in der P4 oder FPGA Entwicklung
- Erfahrung in der Softwareentwicklung, idealerweise z.B. Python/Django/HTML/Javascript

Forschungsfragen, die im Rahmen dieser Arbeit beantwortet werden:

  • wie flexibel kann mit hardwarebasierten Lastgeneratoren Last erzeugt werden?
  • wird durch eine integrierte Lasterzeugung die Genauigkeit beeinflusst?
  • ... weitere Fragestellungen, die im Rahmen der Bearbeitung herausgearbeitet werden ...
Supervisor: Lisa Wernet
KOM-ID: KOM-M-0771 Student: Lukas Schnarz
Link zur Ausschreibung
  • Design und Implementierung geeigneter Mechanismen für den Betrieb heterogener UPFs im 5G Core
  • Evaluation der Auswirkungen des Zu- und Abschaltens von heterogenen UPF Instanzen
  • Evaluation verschiedener Kommunikationsstrategien zwischen heterogenen UPFs und
    anderen Netzwerkfunktionen
Supervisor: Leonhard Balduf
KOM-ID: KOM-B-0718
Link zur Ausschreibung

Problem Description

We maintain a crawler for libp2p-based Kademlia DHTs, which we use to crawl IPFS.
We are able to enumerate all DHT servers, i.e., all nodes participating as server nodes in the DHT.
We can then visualize data about these nodes, such as agent versions, churn, geolocation, etc.

The crawler, however, is generic over all DHTs implemented using go-libp2p-kad-dht.
There are many projects using this implementation, which we could potentially crawl.
They differ in a) their bootstrap nodes, and b) their protocol identifiers.

Main Objective

Working on well-connected Linux servers, set up multiple instances of the crawler, enumerating peers of as many networks as possible.
Set up automated data wrangling and visualization pipelines, displaying the results on a statically-generated website.

Optional Bonus Goals

  • Extend the visualization and data science scripts.
  • Realize the setup in a containerized environment, in a way that is easily extensible with new networks.
  • Save the results of the crawls into a PostgreSQL database for compact storage and easier queries.
  • ???

Prerequisites

You must

  • Have intrinsic motivation to look at P2P networks.
  • Be able to fluently work on remote Linux machines via SSH.
  • Understand Go code.
  • Be able to admin Linux machines and document the setup.

It would probably be helpful if

  • You understood how DHT crawlers work in general.
  • You knew Chinese, since many of the projects have Chinese source code comments or documentation.
  • You knew R, since the evaluation scripts are in R.
  • You were well-versed in SQL and design of database schemas.

Bachelor/Master?

If you pursue this as a Bachelor's thesis, the minimum goal and any number of optional goals apply.

If you pursue this as a Master's thesis, you'll have to significantly extend the data processing and visualization frameworks to derive new interesting data from the datasets. Addiitonally, you'll of course have to implement the setup in a clean way, well-documented, with more of the optional goals realized. I'd also expect you to come up with your own optional goals.

Supervisor: Ahmad Khalil
KOM-ID: KOM-M-0671 Student: Ha Giang Hoang Tran
Link zur Ausschreibung

Motivation

Object detection is a crucial task in the field of autonomous vehicles. To train the object detection models, data such as RGB data is collected either offline or online during the operation of the cars, and this data is used to train the models. However, in supervised learning approaches, which have superior performance, the data needs to be annotated.

Active learning is an approach that aims to reduce the amount of annotated data required. It achieves this by selecting the most informative information from the unannotated data pool. Unfortunately, most active learning approaches for object detection assume that the data is already gathered and located in a central entity (offline). These approaches overlook the consumption of networking resources that occur when transmitting the data in the online data gethering.

In this thesis, the student will have the chance to enable online data collection in a multi-agent system (e.g., vehicular system), while considering the available network resources. The objective is to develop an active learning approach that optimizes the utilization of the network resources while maintaining high object detection performance. This would involve selecting the most informative data for annotation and training, taking into account the limitations of the available network resources.

The goal is to enhance the efficiency and effectiveness of object detection in autonomous vehicles by developing an active learning approach that leverages the available network resources without compromising the performance of the models.

Task

This thesis is open for master/bachelor students, and the main tasks are as follows:
•    Explore relevant existing related literature.
•    Formalizing the problem and modeling the solution and
•    Setting up a development environment.
•    Evaluation of the proposed solution in comparison to the existing state-of-the-art approaches.

Prerequisites

It is required to have:
•    Motivated and individual working style
•    Very good programming skills in Python
•    Very good machine learning / Deep learning knowledge

Related Literature

[1] Yu, Weiping, et al. "Consistency-based active learning for object detection." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022.

[2] Choi, Jiwoong, et al. "Active learning for deep object detection via probabilistic modeling." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021.

[3] Feng, Di, et al. "A review and comparative study on probabilistic object detection in autonomous driving." IEEE Transactions on Intelligent Transportation Systems 23.8 (2021): 9961-9980.

Supervisor: Ahmad Khalil
KOM-ID: KOM-M-0772 Student: Kristina Raysbikh
Link zur Ausschreibung

Motivation 


Object detection holds paramount importance in vehicular applications, serving as a cornerstone for road safety, efficient traffic management, and the evolution of autonomous driving systems. However, while training the object detection models in vehicular applications, the intricacies of data privacy, constrained communication bandwidth, and computational limitations present significant challenges. This thesis aims to leverage the potential of adaptive model compression techniques to augment Federated Learning-based object detection model training in vehicular applications. The central premise is to develop compression strategies (such as pruning, and quantization) that dynamically respond to contextual information, such as weather conditions, traffic density, and time of day, to optimize model training efficiency and accuracy. This integration holds the potential to create more resource-efficient model training that can adapt to changing environments and perform optimally under varying conditions.


Task

This thesis is open for master's students, and the main tasks are as follows:
•    Explore relevant existing related literature.
•    Formalizing the problem and modeling the solution and
•    Setting up a development environment and implementing the proof of concept or prototype.
•    Evaluation of the proposed solution in comparison to the existing state-of-the-art approaches.

Prerequisites

It is required to have:
•    Motivated and individual working style
•    Very good programming skills in Python.
•    Very good machine learning / Deep learning knowledge

Contact

I am looking for motivated students interested in working on cutting-edge technologies and vehicular applications area. If you are interested in writing a Bachelor's or Master's Thesis, please feel free to contact me at Ahmad Khalil (ahmad.khalil(at)kom.tu-darmstadt.de). You can email me the following information:
•    Your CV or small text about your courses and prior experiences in this area.

Supervisor: Ahmad Khalil
KOM-ID: KOM-M-0767 Student: Tizian Dege
Link zur Ausschreibung

Motivation 

As the development of autonomous vehicles continues to progress, the need for improving detection models becomes increasingly important. However, sharing the data gathered from vehicles with a central server can raise privacy concerns and consume a significant amount of network capacity. Federated learning provides a solution to this challenge by allowing for the training of machine learning models on decentralized data, without the need to transfer raw data to a central server. However, federated learning inherits convergence issues, especially when the data is non-iid (non-independent and identically distributed). This makes studying how the data distribution influences the performance of perception models trained in a federated learning setup in the vehicular collective perception field crucial. By analyzing state-of-the-art approaches and developing novel methods that take into account the data heterogeneity, the student will help improving the accuracy and reliability of perception models. Moreover, training models locally in the vehicle poses significant challenges that require careful consideration, such as dealing with the labeling issue of data collected in real-time. The student will have the opportunity to address these challenges and contribute to the future of autonomous vehicles.

Task

This thesis is open for master's students, and the main tasks are as follows:
•    Explore relevant existing related literature.
•    Formalizing the problem and modeling the solution and
•    Setting up a development environment and implementing the proof of concept or prototype.
•    Evaluation of the proposed solution in comparison to the existing state-of-the-art approaches.

Prerequisites

It is required to have:
•    Motivated and individual working style
•    Very good programming skills in Python.
•    Very good machine learning / Deep learning knowledge

Contact

I am looking for motivated students interested in working on cutting-edge technologies and vehicular applications area. If you are interested in writing a Bachelor's or Master's Thesis or gaining experience in MAKI project as HiWi, please feel free to contact me at Ahmad Khalil (ahmad.khalil(at)kom.tu-darmstadt.de). You can email me the following information:
•    Your CV or small text about your courses and prior experiences in this area.