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Optimal User-Edge Assignment in Hierarchical Federated Learning Based on Statistical Properties and Network Topology Constraints
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IEEE Computer Society
, 2022 , Article)
Distributed learning algorithms aim to leverage distributed and diverse data stored at users' devices to learn a global phenomena by performing training amongst participating devices and periodically aggregating their local ...
Deep Reinforcement Learning for Network Selection over Heterogeneous Health Systems
(
IEEE Computer Society
, 2022 , Article)
Smart health systems improve our quality oflife by integrating diverse information and technologies into health and medical practices. Such technologies can significantly improve the existing health services. However, ...
Communication-efficient hierarchical federated learning for IoT heterogeneous systems with imbalanced data
(
Elsevier B.V.
, 2022 , Article)
Federated Learning (FL) is a distributed learning methodology that allows multiple nodes to cooperatively train a deep learning model, without the need to share their local data. It is a promising solution for telemonitoring ...
Distributed CNN Inference on Resource-Constrained UAVs for Surveillance Systems: Design and Optimization
(
Institute of Electrical and Electronics Engineers Inc.
, 2022 , Article)
Unmanned aerial vehicles (UAVs) have attracted great interest in the last few years owing to their ability to cover large areas and access difficult and hazardous target zones, which is not the case of traditional systems ...