FedMes: Speeding up federated learning with multiple edge servers
… In Section II, we describe our problem setup with multiple edge servers. The proposed FedMes
algorithm is described in Section III and its convergence bound is analyzed in Section IV. …
algorithm is described in Section III and its convergence bound is analyzed in Section IV. …
Federated learning in mobile edge networks: A comprehensive survey
… to the training of ML models in general, we focus specifically on DNN model training in this
section as a majority of the papers that we subsequently review study the federated training of …
section as a majority of the papers that we subsequently review study the federated training of …
Toward multiple federated learning services resource sharing in mobile edge networks
… model of multiple federated learning services at the multi-access edge computing server. …
resources among learning services at each mobile device for the local training process and …
resources among learning services at each mobile device for the local training process and …
Multi-edge server-assisted dynamic federated learning with an optimized floating aggregation point
B Ganguly, S Hosseinalipour, KT Kim… - IEEE/ACM …, 2023 - ieeexplore.ieee.org
… trusted servers. To respond to the aforementioned challenges, we propose cooperative
edge-assisted dynamic/online federated learning (… of the edge servers in ML model training, and …
edge-assisted dynamic/online federated learning (… of the edge servers in ML model training, and …
Bandwidth allocation for multiple federated learning services in wireless edge networks
… Abstract—This paper studies a federated learning (FL) system, where multiple FL services
co-exist in a wireless network and share common wireless resources. It fills the void of …
co-exist in a wireless network and share common wireless resources. It fills the void of …
Confederated learning: Federated learning with decentralized edge servers
… a multi-server based FL framework, whereby the servers … each server is connected to an
individual set of edge devices. … of sovereign servers united for the purpose of learning a global …
individual set of edge devices. … of sovereign servers united for the purpose of learning a global …
EdgeFed: Optimized federated learning based on edge computing
… by edge computing, we proposed edge federated learning (… in the edge server to improve
the learning efficiency and … from mobile clients to the edge server, the computational cost …
the learning efficiency and … from mobile clients to the edge server, the computational cost …
Toward resource-efficient federated learning in mobile edge computing
… This article first illustrates the typical use cases of federated learning in mobile edge …
approaches in federated learning. The resource-efficient techniques for federated learning are …
approaches in federated learning. The resource-efficient techniques for federated learning are …
Client-edge-cloud hierarchical federated learning
… system, supported with a HierFAVG algorithm that allows multiple edge servers to perform
… FEDERATED LEARNING SYSTEMS In this section, we first introduce the general learning …
… FEDERATED LEARNING SYSTEMS In this section, we first introduce the general learning …
In-network computation for large-scale federated learning over wireless edge networks
… at the server, delaying the training process… edge network architecture that enables
decentralizing the model aggregation process at the server, thereby significantly reducing the …
decentralizing the model aggregation process at the server, thereby significantly reducing the …
相关搜索
- hierarchical federated learning
- federated learning client selection
- federated learning in edge computing
- federated learning in mobile edge networks
- federated learning wireless edge networks
- federated learning with non-iid data
- dynamic federated learning
- decentralized federated learning
- asynchronous federated learning
- multiple federated learning services bandwidth allocation
- edge servers confederated learning
- federated learning mobile edge computing systems
- federated learning wireless edge systems
- federated learning edge intelligence
- federated learning edge devices
- convergence of federated learning