# Distributed Deep Convolutional Neural Networks for the Internet-of-Things

arXiv: https://arxiv.org/abs/1908.01656 · HTML: https://mlsanity.com/paper/1908.01656/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2019.

## Built on by (most cited first, 13 of 13)

- [A Survey on Collaborative DNN Inference for Edge Intelligence](https://mlsanity.com/paper/2207.07812/) (2022)
- [Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence](https://mlsanity.com/paper/2105.01798/) (2021)
- [Distributed CNN Inference on Resource-Constrained UAVs for Surveillance Systems: Design and Optimization](https://mlsanity.com/paper/2105.11013/) (2021)
- [Deep Reinforcement Learning for Trajectory Path Planning and Distributed Inference in Resource-Constrained UAV Swarms](https://mlsanity.com/paper/2212.11201/) (2022)
- [Semantic Edge Computing and Semantic Communications in 6G Networks: A Unifying Survey and Research Challenges](https://mlsanity.com/paper/2411.18199/) (2024)
- [RL-DistPrivacy: Privacy-Aware Distributed Deep Inference for low latency IoT systems](https://mlsanity.com/paper/2208.13032/) (2022)
- [Efficient Real-Time Image Recognition Using Collaborative Swarm of UAVs and Convolutional Networks](https://mlsanity.com/paper/2107.04648/) (2021)
- [LLHR: Low Latency and High Reliability CNN Distributed Inference for Resource-Constrained UAV Swarms](https://mlsanity.com/paper/2305.15858/) (2023)
- [Socialized Learning: A Survey of the Paradigm Shift for Edge Intelligence in Networked Systems](https://mlsanity.com/paper/2404.13348/) (2024)
- [AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices](https://mlsanity.com/paper/2412.00724/) (2024)
- [Achieving Trustworthy Real-Time Decision Support Systems with Low-Latency Interpretable AI Models](https://mlsanity.com/paper/2506.20018/) (2025)
- [DCentNet: Decentralized Multistage Biomedical Signal Classification using Early Exits](https://mlsanity.com/paper/2502.17446/) (2025)
- [Going Beyond the Edge: Distributed Inference of Transformer Models on Ultra-Low-Power Wireless Devices](https://mlsanity.com/paper/2605.15694/) (2026)

## Abstract (arXiv)

Severe constraints on memory and computation characterizing the Internet-of-Things (IoT) units may prevent the execution of Deep Learning (DL)-based solutions, which typically demand large memory and high processing load. In order to support a real-time execution of the considered DL model at the IoT unit level, DL solutions must be designed having in mind constraints on memory and processing capability exposed by the chosen IoT technology. In this paper, we introduce a design methodology aiming at allocating the execution of Convolutional Neural Networks (CNNs) on a distributed IoT application. Such a methodology is formalized as an optimization problem where the latency between the data-gathering phase and the subsequent decision-making one is minimized, within the given constraints on memory and processing load at the units level. The methodology supports multiple sources of data as well as multiple CNNs in execution on the same IoT system allowing the design of CNN-based applications demanding autonomy, low decision-latency, and high Quality-of-Service.