Client-edge-cloud hierarchical federated learning
… We consider a hierarchical FL system with 50 clients, 5 edge servers and a cloud server,
assuming each edge server authorizes the same number of clients with the same amount of …
assuming each edge server authorizes the same number of clients with the same amount of …
Resource-efficient federated learning with hierarchical aggregation in edge computing
… Abstract—Federated learning (FL) has emerged in edge computing to address limited
bandwidth and privacy concerns of traditional cloud-based centralized training. However, the …
bandwidth and privacy concerns of traditional cloud-based centralized training. However, the …
Optimal user-edge assignment in hierarchical federated learning based on statistical properties and network topology constraints
… We consider a hierarchical User-Edge-Cloud learning system architecture shown in Fig. 1.
The UE data is represented by the matrix U(C×U) where C is the number of classes in the …
The UE data is represented by the matrix U(C×U) where C is the number of classes in the …
Decentralized edge intelligence: A dynamic resource allocation framework for hierarchical federated learning
… paradigm known as Federated Learning (FL), which enables … and device dropouts, the
Hierarchical Federated Learning (HFL) … design for end-edge-cloud collaboration is of paramount …
Hierarchical Federated Learning (HFL) … design for end-edge-cloud collaboration is of paramount …
Optimizing training efficiency and cost of hierarchical federated learning in heterogeneous mobile-edge cloud computing
… Experimental Settings In the mobile-edge cloud computing (MECC) system, … a hierarchical
federated learning (FL) framework that includes a central cloud server and 5 sub-mobile edge …
federated learning (FL) framework that includes a central cloud server and 5 sub-mobile edge …
[HTML][HTML] HED-FL: A hierarchical, energy efficient, and dynamic approach for edge Federated Learning
… In [27], authors present a client–edge–cloud hierarchical Federated Learning system … of
hierarchical federated learning, where more layers and aggregation steps are accounted for. …
hierarchical federated learning, where more layers and aggregation steps are accounted for. …
InFEDge: A blockchain-based incentive mechanism in hierarchical federated learning for end-edge-cloud communications
… edge-cloud collaborative architecture to leverage ubiquitous data and resources. Opposite
to centralized intelligence, Hierarchical Federated Learning (… benefits of Federated Learning (…
to centralized intelligence, Hierarchical Federated Learning (… benefits of Federated Learning (…
Flee: A hierarchical federated learning framework for distributed deep neural network over cloud, edge, and end device
… edges, respectively. Meanwhile, an early exit is set to reduce computing resource overhead,
forming a hierarchical … and edges, proposing a hierarchical federated learning framework …
forming a hierarchical … and edges, proposing a hierarchical federated learning framework …
Hierarchical federated learning with quantization: Convergence analysis and system design
… We will then introduce the hierarchical FL system with one cloud server, s edge servers, n …
hierarchical FL system with n clients, s edge servers and a cloud server, assuming that edge …
hierarchical FL system with n clients, s edge servers and a cloud server, assuming that edge …
A distributed hierarchical deep computation model for federated learning in edge computing
H Zheng, M Gao, Z Chen, X Feng - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
… Such a model is commonly used for federated learning [9], [10]. … learning a shared model
via federated learning without transmitting raw data from multiple edge nodes to the cloud …
via federated learning without transmitting raw data from multiple edge nodes to the cloud …
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