A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Filters
HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning
[article]
2020
arXiv
pre-print
Preserved Fulltext
Other Versions
We further formulate a joint computation and communication resource allocation and edge association problem for device users under HFEL framework to achieve global cost minimization. ...
With the optimal policy of the convex resource allocation subproblem for a set of devices under a single edge server, an efficient edge association strategy can be achieved through iterative global cost ...
learning and formulate a holistic joint computation and communication resource allocation and edge association model for global learning cost minimization. • We decompose the challenging global cost minimization ...
arXiv:2002.11343v2
fatcat:x4w4dnk6ebfbbpagfx5plr2us4
CFLMEC: Cooperative Federated Learning for Mobile Edge Computing
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
fills the gap of communication resource allocation for devices with federated learning. ...
, and interference aware communication resource allocation algorithm for less reliance on edge servers (LRs) in D2D link. ...
In HFEL, they studied the resource optimization problem formulated as a global cost minimization, and decomposed it into two sub problems: resource allocation and edge association. Savazzi et al. ...
arXiv:2102.10591v1
fatcat:y4woc4t3incqhp7swsczqviwim
Semi-asynchronous Hierarchical Federated Learning for Cooperative Intelligent Transportation Systems
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
We further formulate a joint edge node association and resource allocation problem under the proposed SHFL framework to prevent personalities of heterogeneous road vehicles and achieve communication-efficiency ...
Therefore, in this paper, we propose a novel Semi-asynchronous Hierarchical Federated Learning (SHFL) framework for C-ITS that enables elastic edge to cloud model aggregation from data sensing. ...
In [15] , the authors develop an importance aware joint data selection and resource allocation algorithm to maximize the resource and learning efficiencies. ...
arXiv:2110.09073v1
fatcat:aqenyck3tneijatgb5xuqry4nu
2020 Index IEEE Transactions on Wireless Communications Vol. 19
2020
IEEE Transactions on Wireless Communications
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
., and Saad, W., Joint Access and Backhaul Resource Management in Satellite-Drone Networks: A Competitive Market Approach; TWC June 2020 3908-3923 Hu, Y.H., see Xia, M., TWC June 2020 3769-3781 Hua, ...
Identification for MIMO Systems in Dynamic Environments; TWC June 2020 3643-3657 Huang, C., see Yang, M., TWC Sept. 2020 5860-5874 Huang, D., Tao, X., Jiang, C., Cui, S., and Lu, J ...
., +, TWC March 2020 2022-2035 HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning. ...
doi:10.1109/twc.2020.3044507
fatcat:ie4rwz4dgvaqbaxf3idysubc54
Context-Aware Online Client Selection for Hierarchical Federated Learning
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Using Edge Servers (ESs) as intermediaries to perform model aggregation in proximity can reduce the transmission overhead, and it enables great potentials in low-latency FL, where the hierarchical architecture ...
Federated Learning (FL) has been considered as an appealing framework to tackle data privacy issues of mobile devices compared to conventional Machine Learning (ML). ...
Yu, “Hfel: Joint edge association and resource allocation for cost-efficient
hierarchical federated edge learning,” IEEE Transactions on Wireless Communications, vol. 19, no. 10, pp. 6535–6548, ...
arXiv:2112.00925v2
fatcat:jdnoe6u2e5coxojxwetjwqq3py