Junaid Farooq

Collaborative Cloud-Edge Computing via LLM-Guided Constrained Reinforcement Learning

Cloud computing (CC) and mobile edge computing (MEC) has become essential for augmenting computational capacity and minimizing latency by offloading tasks from user equipments (UEs) to servers. While deep reinforcement learning (DRL) is a powerful tool for optimizing offloading decisions and managing resource allocatio...

Ensembling personalized quantum models with local differential privacy

Investigation of Bridge Serviceability and Strength Using Integrated Static and Dynamic Approaches

The load-bearing capacity of bridges is critical to their structural integrity and longevity. In this work this capability is assessed using an integrated method combining computational calculations and experimental testing, with focus on static and dynamic loading situations. The numerical part relies on finite elemen...

Dependency-Aware Task Offloading via Quantum Graph Attention Network-based Deep Reinforcement Learning in Collaborative Edge-Cloud Systems

Edge–cloud collaboration has become essential for managing the limited computational capacity of edge servers while meeting the dynamic and latency-critical demands of wireless devices in 5G and beyond networks. However, efficient offloading of interdependent tasks remains challenging due to heterogeneous computing res...

Semantic Compression Trees: Multi-Resolution Knowledge Retrieval via Hierarchical Semantic Residuals

Semantic Compression Trees is introduced, a hierarchical index in which each node stores only its semantic residual -- the information it adds beyond its parent -- and retrieval proceeds by progressive descent from the root, so that per-query cost is governed by tree depth rather than collection size.