5 Hits in 3.5 sec

HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning [article]

Siqi Luo, Xu Chen, Qiong Wu, Zhi Zhou, Shuai Yu
2020 arXiv   pre-print
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]

Xinghan Wang, Xiaoxiong Zhong, Yuanyuan Yang, Tingting Yang
2021 arXiv   pre-print
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]

Qimei Chen and Zehua You and Hao Jiang
2021 arXiv   pre-print
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  
., 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]

Zhe Qu, Rui Duan, Lixing Chen, Jie Xu, Zhuo Lu, Yao Liu
2021 arXiv   pre-print
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