# Machine Learning at the Network Edge: A Survey

## TL;DR

This survey reviews recent research on deploying machine learning models directly on edge devices in IoT networks, highlighting techniques, tools, and hardware to overcome resource constraints and privacy issues.

## Contribution

It provides a comprehensive overview of the current state, challenges, and solutions for implementing machine learning at the network edge in IoT environments.

## Key findings

- Edge deployment reduces latency and communication costs.
- Compression techniques enable efficient ML model deployment on resource-limited devices.
- Various hardware and frameworks support intelligent edge systems.

## Abstract

Resource-constrained IoT devices, such as sensors and actuators, have become ubiquitous in recent years. This has led to the generation of large quantities of data in real-time, which is an appealing target for AI systems. However, deploying machine learning models on such end-devices is nearly impossible. A typical solution involves offloading data to external computing systems (such as cloud servers) for further processing but this worsens latency, leads to increased communication costs, and adds to privacy concerns. To address this issue, efforts have been made to place additional computing devices at the edge of the network, i.e close to the IoT devices where the data is generated. Deploying machine learning systems on such edge computing devices alleviates the above issues by allowing computations to be performed close to the data sources. This survey describes major research efforts where machine learning systems have been deployed at the edge of computer networks, focusing on the operational aspects including compression techniques, tools, frameworks, and hardware used in successful applications of intelligent edge systems.