The integrated AQSE-QDST framework, which combines the Adaptive Quantum Swarm Evolution algorithm for feature space optimization with QDST-Net (Quantum-Inspired Dual Spatial-Temporal Network) as the classification engine, is presented.
Rasoul Farahi, Nahideh Derakhshanfard, Roya Abdollahzadeh Sarnaghi et al.· Discover Internet of Things· 0 citations
The results show a 15% increase in clustering metrics compared to current algorithms, showcasing how the method improves device assignment and data redistribution in hierarchical semi-synchronous federated learning, addressing issues in model accuracy and resource optimization.
Hadi Farajvand, Nahideh Derakhshanfard, Abbas Mirzaei et al.· Scientific Reports· 0 citations
A review of federated learning through a structured taxonomy that covers its core architectural paradigms, major learning types, model training approaches, and aggregation mechanisms, and analyzes the principal challenges confronting FL, including privacy and security risks, statistical and system heterogeneity, commun...
Mahdiyeh Velaei, Hosna Ghahramani, Ali Ghaffari et al.· Cluster Computing· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
