Communication-Efficient Adaptive Federated Split Learning Optimization Method in Edge Intelligence Scenarios

FANG Juan; LI Jiabao

doi:10.11999/JEIT260260

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FANG Juan, LI Jiabao. Communication-Efficient Adaptive Federated Split Learning Optimization Method in Edge Intelligence Scenarios[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260260

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FANG Juan, LI Jiabao. Communication-Efficient Adaptive Federated Split Learning Optimization Method in Edge Intelligence Scenarios[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260260

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Communication-Efficient Adaptive Federated Split Learning Optimization Method in Edge Intelligence Scenarios

doi: 10.11999/JEIT260260 cstr: 32379.14.JEIT260260

FANG Juan^,,
LI Jiabao

Beijing University of Technology, Beijing 100124, China

Funds: The National Natural Science Foundation of China (61202076), The Beijing Natural Science Foundation (4262021)

Received Date: 2026-03-09
Accepted Date: 2026-10-16
Rev Recd Date: 2026-10-13

Available Online: 2026-10-25

Abstract

Abstract

Objective Federated Split Learning (FSL) has emerged as a promising paradigm for Edge Intelligence (EI) scenarios, enabling collaborative training through neural network partitioning to alleviate resource constraints on terminal devices. However, FSL faces critical challenges including high communication overhead introduced by frequent interactions between devices and edge servers and insufficient adaptability to dynamic network environments, which severely restrict deployment efficiency. Moreover, the data heterogeneity in practical edge environments poses severe challenges to the personalized performance of models, and existing research lacks synergistic optimization between personalized accuracy and communication efficiency. This study addresses these challenges by jointly incorporating Reinforcement Learning (RL), multi-strategy communication optimization, and multi-task learning to adapt to network bandwidth variations, reduce communication overhead, and improve personalized model testing accuracy. Methods This paper designs a communication-efficient adaptive FSL framework integrating RL-based adaptive model splitting, multi-strategy communication optimization, and multi-task learning-based personalized optimization. First, this paper proposes a Reinforcement Learning-based Communication-Efficient Adaptive Federated Split Learning (RLCEA-FSL) algorithm. This responds to network bandwidth changes adaptively via RL and reduces communication overhead between end devices and edge servers by multiple communication optimization strategies. Second, a Multi-Task Learning-based Communication-Efficient Personalized Federated Learning (MTLCE-PFL) algorithm is proposed to tackle the challenge of joint optimization between personalized accuracy and communication efficiency under non-independent and identically distributed (Non-IID) data distributions. Specifically, private personalized layers are introduced using batch normalization layers to adapt to local data distributions, transforming the Non-IID problem into a multi-task learning problem. Additionally, quantization compression is applied to intermediate activates transmitted from devices to servers, and dynamic hierarchical gradient compression based on compressed sensing is implemented for gradient backhaul from servers to devices. Results and Discussions The proposed framework and algorithms are evaluated through extensive simulations on CIFAR-10 and CIFAR-100 datasets using VGG-11 and ResNet-9 models. Four baseline methods are adopted, namely classical FL, SplitFed, FedAdapt, and EcoFed (hereinafter referred to in this order). Compared with the baseline methods, RLCEA-FSL (Algorithm 1) achieves up to 101.23 ×, 24.27 ×, 117.61 × speedup and 37.91 % reduction in training latency without and with activation reuse mechanism, respectively (Fig.3). The algorithm reduces overall communication overhead by 72.75 ×, 17.33 ×, 66.62 ×, and 8.1 % compared with the baseline methods, respectively, when bandwidth is constrained (Fig. 6). This improvement results from the adaptive model splitting strategy dynamically adjusting partition points according to bandwidth variations, combined with multi-strategy communication optimization methods. The RL agent trained on VGG-11 can be directly applied to ResNet-9 without retraining, demonstrating favorable results in terms of training latency, test accuracy, and communication cost (Fig. 7, Fig. 8). The MTLCE-PFL algorithm effectively improves personalized testing accuracy under Non-IID data distributions while reducing communication costs and training latency. Compared with other algorithms, MTLCE-PFL (Algorithm 2) achieves faster convergence and higher personalized testing accuracy on both CIFAR-10 and CIFAR-100 datasets with VGG-11 and ResNet-9 models (Fig. 9). This improvement results from the multi-task learning framework with private personalized layers adapting to local data distributions, transforming the Non-IID problem into independent task optimization for each client. In terms of communication efficiency, MTLCE-PFL reduces bidirectional communication overhead by 11.97 ×, 2.86 ×, and 10.33 × compared with classical FL, SplitFed, and FedAdapt, respectively, through quantization-based compression for forward activates and dynamic hierarchical compressed sensing for backward gradients (Fig. 10). For training latency, despite increased computational complexity from personalized layers, MTLCE-PFL reduces average per-round training time by 11.02 % compared with SplitFed, and achieves 4.28 × and 18.68 × speedup compared with FedAdapt and classical FL, respectively (Fig. 11). The results demonstrate the effectiveness and efficiency of the proposed methods in terms of global model accuracy, personalized testing accuracy, communication cost, and training delay. Conclusions The proposed communication-efficient adaptive federated split learning framework addresses the challenges of high communication overhead, dynamic network environments, and data heterogeneity in EI scenarios from multiple perspectives. By integrating RL-based adaptive model splitting with multi-strategy communication optimization, the RLCEA-FSL algorithm adaptively responds to bandwidth variations and significantly reduces training latency and communication costs while maintaining comparable global model accuracy. The cross-model generalization capability of the RL agent enhances practical deployment flexibility, enabling direct transfer between different neural network architectures without retraining. Furthermore, by introducing multi-task learning with private personalized layers, the MTLCE-PFL algorithm effectively improves personalized testing accuracy under Non-IID data distributions while reducing bidirectional communication overhead through quantization and dynamic hierarchical compressed sensing techniques.
- Edge Intelligence,
- Federated Split Learning,
- Personalized Federated Learning,
- Communication Efficiency

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References(23)

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