Theses

Στην εργασία αυτή θα πρέπει να εκτελέσετε (inference) διάφορους αλγόριθμους νευρωνικών δικτύων βαθιάς μάθησης για συγκεκριμένα δεδομένα και κάτω από συγκεκριμένες συνθήκες προκειμένου να καταγράψετε και εν συνεχεία να αναλύσετε τις τιμές επίδοσης διαφόρων συστημάτων (ειδικά Raspberry PI και Jetson Nano). Αναζήτηση συσχετισμών μεταξύ χαρακτηριστικών μοντέλων νευρωνικών δικτύων βαθιάς μάθησης και επίδοσης συστήματος κατά την εκτέλεση του μοντέλου.

Generative Models such as Generative Adversarial Networks (GANs) can be leveraged to generate more realistic adversarial examples.This task focuses on exploring the potential of GANs to create adversarial examples capable of deceiving machine learning models into misclassifying the provided samples.

This task focuses on employing GANs as a defense mechanism against adversarially perturbed samples. A GAN can be trained to model the distribution of clean, unperturbed data. Given a perturbed image, the model finds a close output which does not contain the adversarial changes and, as a result, purifies the attacked sample.

The objective of this task is to utilize Diffusion Models to generate adversarial examples aimed at causing misclassification. Diffusion models can achieve adversarial attacks by incorporating adversarial objectives in their generative process.

In this task, the main purpose is to use Diffusion Models in order to purify adversarial samples. The given adversarial sample is diffused with noise following a forward diffusion process, and then a clean image is recovered following a reverse generative process.

The Model Context Protocol (MCP) is an emerging open standard for integrating external tools, data sources, and reasoning modules with AI models and agents. As MCP-based systems scale, multiple components (clients and servers) may repeatedly request the same data or tool outputs, leading to redundant computation and increased latency. Applying distributed systems concepts such as caching and replication can significantly improve MCP performance, reliability, and scalability. This thesis explores how these mechanisms can be designed and evaluated in the MCP ecosystem.

The Model Context Protocol (MCP) enables standardized communication between AI models, tools, and data sources. Currently, MCP interactions are largely request–response based — a client must explicitly query a server or tool for data. However, many real-world applications (IoT systems, monitoring dashboards, or multi-agent AI environments) benefit from event-driven communication, where components react automatically to data changes or external triggers. Integrating an event-driven (publish–subscribe) mechanism into MCP would enhance its flexibility, reduce polling overhead, and allow tools to respond in real time to updates from other components.

This thesis will investigate the integration of early-exit neural network architectures into collaborative edge - cloud AI systems to enhance computational efficiency and reduce communication overhead. This thesis will explore advanced exiting criteria beyond entropy, such as reinforcement learning-based policies, maximum probability thresholds, and budget-aware mechanisms to improve decision-making under uncertainty. The thesis will quantify the trade-offs between accuracy, latency, and network utilization, comparing selective offloading with traditional non-selective methods. Additionally, it examines the deployment of multiple early-exit models across distributed edge nodes to assess scalability, resource utilization, and potential bottlenecks, ultimately contributing to more efficient, adaptive, and uncertainty-aware AI inference in real-world edge-cloud environments.