1.2k citations · 11.3k across the 254 of their papers we have counts for
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Asynchronous Decentralized Federated Learning over Lossy Wireless Links via Reception- and Age-Aware Aggregation
Chanuka A. S. Hewa Kaluannakkage, Rajkumar Buyya
Decentralized Federated Learning(DFL) enables collaborative model training across wireless edge nodes, including IoT deployments, autonomous vehicles, UAV swarms, and satellite con…
Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics
Abhishek Sawaika, Durga Pritam Suggisetti, Udaya Parampalli +1
Learning with large-scale datasets and information-critical applications, such as in High Energy Physics (HEP), demands highly complex, large-scale models that are both robust and…
DeFRiS: Silo-Cooperative IoT Applications Scheduling via Decentralized Federated Reinforcement Learning
Zhiyu Wang, Mohammad Goudarzi, Mingming Gong +1
Next-generation IoT applications increasingly span across autonomous administrative entities, necessitating silo-cooperative scheduling to leverage diverse computational resources…
Fedcompass: Federated Clustered and Periodic Aggregation Framework for Hybrid Classical-Quantum Models
Yueheng Wang, Xing He, Zinuo Cai +4
Federated learning enables collaborative model training across decentralized clients under privacy constraints. Quantum computing offers potential for alleviating computational and…
Quantum Machine Learning for Cybersecurity: A Taxonomy and Future Directions
Siva Sai, Ishika Goyal, Shubham Sharma +3
The increasing number of cyber threats and rapidly evolving tactics, as well as the high volume of data in recent years, have caused classical machine learning, rules, and signatur…
AirFed: A Federated Graph-Enhanced Multi-Agent Reinforcement Learning Framework for Multi-UAV Cooperative Mobile Edge Computing
Zhiyu Wang, Suman Raj, Rajkumar Buyya
Multiple Unmanned Aerial Vehicles (UAVs) cooperative Mobile Edge Computing (MEC) systems face critical challenges in coordinating trajectory planning, task offloading, and resource…