Synergistic Optimization Method for Adaptive Hierarchical Federated Learning in Hetero-geneous Edge Environments

Feng Yiming; Qian Zhen; Li Guanghui; Dai Chenglong

doi:10.7544/issn1000-1239.202550146

Journal of Computer Research and Development > 2025 > Corrected proof > DOI: 10.7544/issn1000-1239.202550146 CSTR: 32373.14.issn1000-1239.202550146

Feng Yiming, Qian Zhen, Li Guanghui, Dai Chenglong. Synergistic Optimization Method for Adaptive Hierarchical Federated Learning in Hetero-geneous Edge Environments[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202550146

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Synergistic Optimization Method for Adaptive Hierarchical Federated Learning in Hetero-geneous Edge Environments

School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu 214122

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This work was supported by the National Natural Science Foundation of China (62372214).

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Author Bio:
Feng Yiming: born in 1996. PhD candidate. His main research interests include edge computing and AIoT

Qian Zhen: born in 1996. PhD candidate. Her main research interests include edge caching and federated learning

Li Guanghui: born in 1970. PhD, professor, PhD supervisor. Senior member of CCF. His main research interests include wireless sensor network, edge computing, and intelligent nondestructive detection technology

Dai Chenglong: born in 1992. PhD, associate professor, master supervisor. His main research interests include electroencephalogram processing and electroencephalogram analyzing
Received Date: February 28, 2025
Revised Date: April 09, 2025
Available Online: April 14, 2025

Graphical Abstract

Abstract

Abstract

Traditional hierarchical federated learning (HFL) encounters significant challenges in real world due to device heterogeneity, data heterogeneity (e.g., variations in data volume and feature distribution), and communication resource constraints. Device heterogeneity results in inefficient cross-device collaboration during model training, whereas data heterogeneity induces accuracy degradation and diminished generalization capabilities in the global model. To address these limitations while maximizing the utilization of computation, communication, and data resources in the heterogeneous edge networks, we propose an adaptive synergistic method for hierarchical federated learning. This method synergistically integrates model partitioning and client selection under hardware resource constraints, communication bottlenecks, and non-independent and identically distributed (Non-IID) data conditions to accelerate federated learning training while enhancing model accuracy and adaptability across heterogeneous environments. To quantify the influence of local datasets on global model convergence, a data contribution metric is introduced to evaluate the consistency of client contributions. Furthermore, by integrating deep reinforcement learning (DRL) with real-time resource monitoring and data contribution quantification, the DRL agent dynamically optimizes client selection and edge-cloud collaborative model partitioning strategies prior to each training iteration. This adaptive mechanism leverages system resource availability (e.g., bandwidth, device status) and local data contribution scores to derive optimal policies, thereby accelerating training convergence and enhancing global model accuracy. Simulation results demonstrate that the proposed method achieves significant improvements in model accuracy and training efficiency compared with baseline methods, while exhibiting robust adaptability across diverse heterogeneous environment configurations.
- hierarchical federated learning,
- heterogeneous edge computing,
- model partitioning,
- client selection,
- deep reinforcement learning

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References (36)

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