Joint Optimization of Convergence and Latency for Hierarchical Federated Learning Over Wireless Networks

This letter investigates an hierarchical federated learning (HFL) framework with partial aggregation in a resource-constrained multi-tier wireless network. Due to device heterogeneity and resource scarcity, device selection and resource allocation are jointly considered in the presence of inter-cell...

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Bibliographic Details
Published in:IEEE wireless communications letters 2024-03, Vol.13 (3), p.691-695
Main Authors: Sun, Haofeng, Tian, Hui, Zheng, Jingheng, Ni, Wanli
Format: Article
Language:English
Subjects:
Central processing units
Convergence
convergence analysis
CPUs
Energy consumption
Federated learning
Genetic algorithms
Heterogeneity
Hierarchical federated learning
Ink
Linear programming
Machine learning
Mixed integer
Network latency
Nonlinear programming
Optimization
Performance evaluation
Resource allocation
Resource management
Training
Upper bounds
Wireless networks
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Summary:This letter investigates an hierarchical federated learning (HFL) framework with partial aggregation in a resource-constrained multi-tier wireless network. Due to device heterogeneity and resource scarcity, device selection and resource allocation are jointly considered in the presence of inter-cell interference (ICI). To capture the impacts of device selection and resource allocation on the convergence behavior of HFL, an upper bound of the expected optimality gap is derived. Then, a mixed-integer non-linear programming (MINLP) problem is formulated to minimize the weighted sum of the optimality gap and overall latency. To solve the MINLP problem efficiently, a two-layer solution based on genetic algorithm and alternating optimization is designed to jointly optimize the resource block association, uplink transmission power, and central processing unit frequency allocation. Simulation results on two real-world datasets demonstrate that the proposed algorithm achieves better learning performance and ICI management than the federated learning with client selection (FedCS) and random resource block association schemes.
ISSN:2162-2337
2162-2345
DOI:10.1109/LWC.2023.3339851