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), 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. By partitioning neural networks, FSL enables collaborative training while alleviating resource constraints on terminal devices. However, frequent interactions between devices and edge servers create high communication overhead, and existing FSL methods have limited adaptability to dynamic network environments. These challenges restrict deployment efficiency. In addition, data heterogeneity in practical edge environments can degrade personalized model performance, while existing studies have not jointly optimized personalized test accuracy and communication efficiency. This study addresses these challenges by integrating Reinforcement Learning (RL), multi-strategy communication optimization, and multi-task learning to adapt to network bandwidth variations, reduce communication overhead, and improve personalized test accuracy. Methods This study proposes a communication-efficient adaptive FSL framework that integrates RL-based adaptive model splitting, multi-strategy communication optimization, and multi-task learning-based personalization. First, the Reinforcement Learning-based Communication-Efficient Adaptive Federated Split Learning (RLCEA-FSL) algorithm is proposed to adjust model partition points adaptively in response to network bandwidth variations and reduce communication overhead between end devices and edge servers through multiple communication optimization strategies. Second, the Multi-Task Learning-based Communication-Efficient Personalized Federated Learning (MTLCE-PFL) algorithm is proposed to address the joint optimization of personalized test accuracy and communication efficiency under Non-Independent and Identically Distributed (Non-IID) data distributions. Specifically, batch normalization layers serve as private personalized layers to adapt models to local data distributions, thereby formulating the Non-IID setting as a multi-task learning problem. Additionally, quantization-based compression is applied to intermediate activations transmitted from devices to servers, while dynamic hierarchical gradient compression based on Compressed Sensing (CS) is used to reduce the communication overhead of gradient transmission from servers to devices. Results and Discussions The proposed framework and algorithms are evaluated on the CIFAR-10 and CIFAR-100 datasets using VGG-11 and ResNet-9 models. Four baseline methods are considered: classical Federated Learning (FL), SplitFed, FedAdapt, and EcoFed. With activation reuse enabled, RLCEA-FSL (Algorithm 1) achieves speedups of up to 101.23×, 24.27×, and 117.61× compared with FL, SplitFed, and FedAdapt, respectively. Without activation reuse, it reduces average per-round training time by 37.91% compared with EcoFed (Fig. 3). Under bandwidth-constrained conditions, RLCEA-FSL reduces overall communication overhead by 72.75×, 17.33×, and 66.62× compared with FL, SplitFed, and FedAdapt, respectively, and by 8.1% compared with EcoFed (Fig. 6). These improvements result from the adaptive model-splitting strategy, which adjusts partition points in response to bandwidth variations, combined with multiple communication optimization strategies. The RL agent trained on VGG-11 can also be applied directly to ResNet-9 without retraining, yielding favorable results in training latency, test accuracy, and communication overhead (Figs. 7 and 8). MTLCE-PFL improves personalized test accuracy under Non-IID data distributions while reducing communication overhead and training latency. Compared with the other algorithms, MTLCE-PFL (Algorithm 2) achieves faster convergence and higher personalized test accuracy on both CIFAR-10 and CIFAR-100 with both VGG-11 and ResNet-9 (Fig. 9). These improvements result from the multi-task learning framework and private personalized layers, which adapt to local data distributions and enable client-specific optimization. In terms of communication efficiency, MTLCE-PFL reduces bidirectional communication overhead by 11.97×, 2.86×, and 10.33× compared with FL, SplitFed, and FedAdapt, respectively, through quantization-based compression of intermediate activations and dynamic hierarchical CS-based compression of gradients transmitted back to devices (Fig. 10). Despite the additional computational complexity introduced by personalized layers, MTLCE-PFL reduces average per-round training time by 11.02% compared with SplitFed and achieves speedups of 4.28× and 18.68× over FedAdapt and FL, respectively (Fig. 11). These results demonstrate the effectiveness of the proposed methods in maintaining global model accuracy while improving personalized test accuracy and reducing communication overhead and training latency. Conclusions The proposed communication-efficient adaptive FSL framework addresses high communication overhead, dynamic network conditions, and data heterogeneity in EI scenarios through complementary optimization strategies. RLCEA-FSL combines RL-based adaptive model splitting with communication optimization to respond to bandwidth variations and reduce training latency and communication overhead while maintaining global model accuracy comparable to that of the baseline methods. The direct application of the RL agent trained on VGG-11 to ResNet-9 without retraining suggests potential deployment flexibility; however, further experiments are needed to establish robust cross-model generalization. MTLCE-PFL combines multi-task learning with private personalized layers to improve personalized test accuracy under Non-IID data distributions while reducing bidirectional communication overhead through quantization-based activation compression and dynamic hierarchical CS-based gradient compression.
- Edge Intelligence (EI),
- Federated Split Learning (FSL),
- Personalized Federated Learning (PFL),
- Communication Efficiency (CE)

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References(23)

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