# Collaborative Inference for AI-Empowered IoT Devices

arXiv: https://arxiv.org/abs/2207.11664 · HTML: https://mlsanity.com/paper/2207.11664/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2022.

## Built on by (most cited first, 15 of 15)

- [Attention-aware Semantic Communications for Collaborative Inference](https://mlsanity.com/paper/2404.07217/) (2024)
- [Private Collaborative Edge Inference via Over-the-Air Computation](https://mlsanity.com/paper/2407.21151/) (2024)
- [Learned Point Cloud Compression for Classification](https://mlsanity.com/paper/2308.05959/) (2023)
- [Remote Inference over Dynamic Links via Adaptive Rate Deep Task-Oriented Vector Quantization](https://mlsanity.com/paper/2501.02521/) (2025)
- [Efficient Feature Compression for Machines with Global Statistics Preservation](https://mlsanity.com/paper/2512.09235/) (2025)
- [Limited Communications Distributed Optimization via Deep Unfolded Distributed ADMM](https://mlsanity.com/paper/2309.14353/) (2023)
- [Privacy-Aware Joint DNN Model Deployment and Partitioning Optimization for Collaborative Edge Inference Services](https://mlsanity.com/paper/2502.16091/) (2025)
- [Scalable Human-Machine Point Cloud Compression](https://mlsanity.com/paper/2402.12532/) (2024)
- [Collaborative Inference over Wireless Channels with Feature Differential Privacy](https://mlsanity.com/paper/2410.19917/) (2024)
- [Distributed Learning and Inference Systems: A Networking Perspective](https://mlsanity.com/paper/2501.05323/) (2025)
- [Compressive Feature Selection for Remote Visual Multi-Task Inference](https://mlsanity.com/paper/2405.09077/) (2024)
- [Collaborative Inference Acceleration with Non-Penetrative Tensor Partitioning](https://mlsanity.com/paper/2501.04489/) (2025)
- [Adversary-Aware Private Inference over Wireless Channels](https://mlsanity.com/paper/2510.20518/) (2025)
- [Understanding Rate-Distortion Performance in Distributed Transformer Inference](https://mlsanity.com/paper/2601.22002/) (2026)
- [MD-AirComp+: Adaptive Quantization for Blind Massive Digital Over-the-Air Computation](https://mlsanity.com/paper/2602.18332/) (2026)

## Abstract (arXiv)

Artificial intelligence (AI) technologies, and particularly deep learning systems, are traditionally the domain of large-scale cloud servers, which have access to high computational and energy resources. Nonetheless, in Internet-of-Things (IoT) networks, the interface with the real-world is carried out using edge devices that are limited in hardware and can communicate. The conventional approach to provide AI processing to data collected by edge devices involves sending samples to the cloud, at the cost of latency, communication, connectivity, and privacy concerns. Consequently, recent years have witnessed a growing interest in enabling AI-aided inference on edge devices by leveraging their communication capabilities to establish collaborative inference. This article reviews candidate strategies for facilitating the transition of AI to IoT devices via collaboration. We identify the need to operate in different mobility and connectivity constraints as a motivating factor to consider multiple schemes, which can be roughly divided into methods where inference is done remotely, i.e., on the cloud, and those that infer on the edge. We identify the key characteristics of each strategy in terms of inference accuracy, communication latency, privacy, and connectivity requirements, providing a systematic comparison between existing approaches. We conclude by presenting future research challenges and opportunities arising from the concept of collaborative inference.