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Federated reinforcement learning for edge computing optimization – Complete Phd and Masters Thesis

[ad_1] Introduction With the rapid growth of Internet of Things (IoT) devices and the increasing demand for real-time data processing, edge computing has emerged as a promising solution to address the limitations of cloud computing.…

Neuromorphic computing for spiking neural networks – Complete Phd and Masters Thesis

[ad_1] Introduction Neuromorphic computing has emerged as a promising approach to developing brain-inspired computing systems that mimic the behavior of the human brain. Spiking neural networks, which are based on the principles of biological neural…

Homomorphic encryption for secure image processing – Complete Phd and Masters Thesis

[ad_1] Introduction Homomorphic encryption is a revolutionary technology that allows computations to be performed directly on encrypted data without the need to decrypt it. This breakthrough in cryptography has significant implications for secure image processing,…

Secure multi-party computation for privacy-preserving federated learning – Complete Phd and Masters Thesis

[ad_1] Introduction In recent years, federated learning has gained significant attention as a privacy-preserving technique for training machine learning models on distributed data sources. However, one of the key challenges in federated learning is ensuring…

Reinforcement learning for self-driving vehicles – Complete Phd and Masters Thesis

[ad_1] Introduction: Self-driving vehicles are an emerging technology that has the potential to transform transportation systems and improve road safety. Reinforcement learning, a subset of machine learning, plays a crucial role in enabling these vehicles…

Explainable deep learning for time series forecasting – Complete Phd and Masters Thesis

[ad_1] Introduction In recent years, deep learning has become a popular and powerful approach for solving complex machine learning problems, including time series forecasting. However, one of the main challenges with deep learning models is…

Zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) for privacy-preserving auctions – Complete Phd and Masters Thesis

[ad_1] Introduction Zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) are powerful cryptographic tools that allow one party to prove to another party the knowledge of certain information without revealing the information itself. This technology has…

Quantum-inspired optimization for network resource allocation – Complete Phd and Masters Thesis

[ad_1] Introduction Quantum-inspired optimization has emerged as a promising approach for solving complex optimization problems in various domains. In the context of network resource allocation, the efficient utilization of resources is crucial for ensuring high…

Multi-modal learning for visual-textual alignment – Complete Phd and Masters Thesis

[ad_1] Introduction Multi-modal learning for visual-textual alignment is a rapidly growing research area that aims to bridge the gap between different modalities, such as images and text, in order to improve the performance of various…

Differential privacy for privacy-preserving social network analysis – Complete Phd and Masters Thesis

[ad_1] Introduction In today’s digital age, the amount of data generated through social networks is enormous, making it an invaluable resource for researchers in various fields such as sociology, computer science, and economics. However, this…

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