HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning
By Siqi Luo, Xu Chen, Qiong Wu, Zhi Zhou, Shuai Yu. 2020
426 citations recorded by the source corpus.
Federated Learning (FL) has been proposed as an appealing approach to handle data privacy issue of mobile devices compared to conventional machine learning at the remote cloud with raw user data uploading. By leveraging edge servers as intermediaries to perform partial model aggregation in proximity and relieve core network transmission overhead, it enables great potentials in low-latency and energy-efficient FL. Hence we introduce a novel Hierarchical Federated Edge Learning (HFEL) framework in which model aggregation is partially migrated to edge servers from the cloud. 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. To solve the problem, we propose an efficient resource scheduling algorithm in the HFEL framework. It can be decomposed into two subproblems: resource allocation given a scheduled set of devices for each edge server and edge association of device users across all the edge servers. 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 reduction adjustment process, which is shown to converge to a stable system point. Extensive performance evaluations demonstrate that our HFEL framework outperforms the proposed benchmarks in global cost saving and achieves better training performance compared to conventional federated learning.
Subfields: Artificial Intelligence, Computer Networks and Communications
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Mingzhe Chen
- H. Vincent Poor
- Zhaohui Yang
- Shuguang Cui
- Hakima Ould-Slimane
- Azzam Mourad
- Mohsen Guizani
- Kai‐Kit Wong
In Computer Networks and Communications
- H. Vincent Poor
- Shuguang Cui
- Mingzhe Chen
- Zhaohui Yang
- Mohsen Guizani
- Kai‐Kit Wong
- Rui Zhang
- Walid Saad
Citations in the corpus
- Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge Based Framework (2020, neutral): “If we further conduct local model aggregation at the edge server, it also helps to reduce the communication overhead by avoiding massive devices to directly communicate with the cloud server over the expensive backbone network bandwidth [15].”
- Federated Learning for 6G: Applications, Challenges, and Opportunities (2021, background): “It is important to consider the multicell and multi-hop FL implementations for real scenarios [89].”
- A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond (2021, background): “Luo et al. [132] introduced a hierarchical federated edge learning framework, in which model aggregation is partially offloaded to edge servers from the cloud.”