mlsanityData to 7 Oct 2026

COMSPLIT: A Communication-Aware Split Learning Design for Heterogeneous IoT Platforms

arXiv 2410.19375 ↗First posted 25 Oct 2024cs.NI cs.DC cs.LGcited by 5

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Abstract (arXiv)

The significance of distributed learning and inference algorithms in Internet of Things (IoT) network is growing since they flexibly distribute computation load between IoT devices and the infrastructure, enhance data privacy, and minimize latency. However, a notable challenge stems from the influence of communication channel conditions on their performance. In this work, we introduce COMSPLIT: a novel communication-aware design for split learning (SL) and inference paradigm tailored to processing time series data in IoT networks. COMSPLIT provides a versatile framework for deploying adaptable SL in IoT networks affected by diverse channel conditions. In conjunction with the integration of an early-exit strategy, and addressing IoT scenarios containing devices with heterogeneous computational capabilities, COMSPLIT represents a comprehensive design solution for communication-aware SL in IoT networks. Numerical results show superior performance of COMSPLIT compared to vanilla SL approaches (that assume ideal communication channel), demonstrating its ability to offer both design simplicity and adaptability to different channel conditions.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 41 of its 58 references that are in the map. First posted on arXiv 25 Oct 2024.

Builds on

41 of 58 references.

References · 41

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29 more
  1. 2020Unleashing the Tiger: Inference Attacks on Split Learningcited by 104
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Built on by

Cited by year

Citing papers per year: 2024: 1; 2025: 4.

5 papers.

  1. 2024The Impact of Cut Layer Selection in Split Federated Learningcited by 6
  2. 2025Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devicescited by 1
  3. 2025Fast AI Model Partition for Split Learning over Edge Networkscited by 1
  4. 2025Pigeon-SL: Robust Split Learning Framework for Edge Intelligence under Malicious Clientscited by 0
  5. 2025Server-Enforced Watermarking in U-Shaped Split Federated Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Hetero-SplitEE: Split Learning of Neural Networks with Early Exits for Heterogeneous IoT Devices
  2. 2024Federated Learning for Collaborative Inference Systems: The Case of Early Exit Networks
  3. 2025Mixture-of-Experts for Distributed Edge Computing with Channel-Aware Gating Function
  4. 2024BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT
  5. 2022Design and Prototyping Distributed CNN Inference Acceleration in Edge Computing
  6. 2025Memory- and Latency-Constrained Inference of Large Language Models via Adaptive Split Computing
  7. 2024Diffusion Models as Network Optimizers: Explorations and Analysis
  8. 2026TSFLora: Token-Compressed Split Fine-Tuning for Wireless Edge Networks