For the last couple of years, the use of IoT devices has become much more widespread with the digital transformation of manufacturing, energy, healthcare, transportation, and similar industries.
In fact, in 2022, there were more than 16.4 billion connected IoT (Internet of Things) devices worldwide and this number is expected to skyrocket to 30.9 billion by 2025.
Still, utilising traditional cloud computing methods to deploy AI models in IoT environments is too complex, causing latency, and ineffective in protecting privacy. Therefore, Edge AI technology shines as an automation-enabling solution to improving operational efficiency and creating AI models in a more private, secure, cost-effective, and data-efficient way.
Despite its many benefits, without combining with federated learning, Edge AI has some limitations, especially in terms of privacy and data accuracy at local levels.
If you are looking for a resource that explains beyond the basics of Edge AI, you are in luck! Through this article, we will explain how utilising federated learning in IoT systems can unlock the full potential of Edge AI applications and why OctaiPipe is your key to achieving it.
What Is Edge AI and How Does it Work?
Before having a more in-depth look into Edge AI and its relationship with federated machine learning, let’s evaluate what it stands for.
Simply put, Edge AI, otherwise known as Edge Artificial Intelligence, is a technology that combines the power of artificial intelligence with the flexibility and scalability of edge computing.
In contrast to traditional practices in which AI applications are developed and run entirely on the Cloud, Edge computing functions bring computation and data storage as close to the point of request as possible to enable local processing on devices or dedicated Edge servers.
This means with Edge AI, machine learning and AI algorithms can run directly on the Edge, allowing operations to not be affected by latency or bandwidth issues. This also helps preserve sensitive data by avoiding sending it over the internet.
By bringing high-performance computing capabilities to the Edge and enabling local AI applications, it makes real-time data processing possible in critical situations, reduces latency, and increases security, scalability, and versatility.
Why Edge AI Is Significant for IoT
Typically, IoT environments consist of a large number of devices, sensors, and actuators to generate and collect data. This data is often high-volume and generated at high velocity, which could make it difficult to manage and analyse.
Edge AI addresses challenges posed by the complexity and heterogeneity of IoT in numerous ways.
Firstly, it brings artificial intelligence and machine learning capabilities closer to the IoT devices and sensors to allow real-time data processing and analysis.
Secondly, it reduces the amount of data that needs to be transmitted to the Cloud or central server for further processing.
This also addresses the security concerns in IoT systems a bit further than traditional cloud computing systems, as Edge networks filter the data and send only what’s important to the Cloud.
Additionally, as it processes data with local models on the Edge, it reduces the power and bandwidth requirements of IoT devices, making it possible to deploy IoT systems in remote and challenging environments where connectivity is limited or unavailable.
Current Challenges in Edge AI Technology
Despite the fact that Edge AI is an attractive solution for applications that require low latency and high speed, such as autonomous vehicles and industrial IoT, there are still disadvantages and challenges associated with its use.
Here is an in-depth look at the challenges of Edge AI.
Data Loss and Quality
As Edge AI systems delete unhelpful or processed data after processing to optimise storage costs, it may cause necessary data to be lost or discarded. The main problem with this setup is that AI might consider beneficial data that is stored in the Cloud as unnecessary.
Also, in terms of data quality, especially in IoT systems, when sensors and devices are not well maintained to collect high-quality data with large samples, Edge AI devices can create low-quality AI algorithms.
Limited Computational Power
When Edge devices are utilised alone in Edge computing setups, they don’t perform as powerfully as the data servers connected to them. Because of this, Edge devices can only perform on-device inference with smaller models or handle small transfer learning tasks.
This limited computational power can lead AI algorithms to be less efficient by running on devices with limited memory and processing power.
Security Vulnerabilities
It is often true that Edge AI setups are more secure than cloud-based AI setups at enterprise levels. Still, on local levels, security is something that Edge AI falls short of providing without federating machine learning on the Edge.
Even though Edge AI minimises security risks by transferring a limited amount of data, it still stores large amounts of training data, including sensitive personal data in Edge Cloud storage, making it attractive for adversarial and confidentiality attacks.
OctaiPipe: The Ultimate Solution to Edge AI Challenges
Now that we have broken down the essentials of Edge AI, let’s explore how OctaiPipe helps organisations overcome these challenges of deploying AI models.
OctaiPipe is a cutting-edge, on-device machine learning platform designed for developing, deploying, and managing AI solutions in industrial IoT environments.
It is the one and only solution in the market that combines federated machine learning, Edge ML, and ML-Ops capabilities to federate on-device learning, enabling delivery and training of more private, cost-efficient, fast, and autonomous Edge AI models in IoT.
Here is a closer look at the critical features of OctaiPipe.
Federated Learning
Federated learning is the key technology for industrial IoT applications to exceed the limits of centralised AI. Basically, with its decentralised nature, it eliminates the need for moving large amounts of data to the models or storing them. At the same time, it allows a group of decentralised Edge devices or systems to train and deploy machine learning models themselves.
This way, it prevents the high costs of network data transfer and cloud computation and allows on-device Edge intelligence without compromising privacy, which is essential to deploying more secure models at local levels.
Edge ML Solutions
The OctaiPipe platform includes an ecosystem of built-in machine learning algorithms and pre-packaged models that run directly on Edge devices. This feature is especially useful for deploying and training new models while ensuring high privacy and security to solve high-value Edge AI use cases.
Edge ML-Ops
By including an automated ML-Ops system, OctaiPipe allows data scientists to manage and scale ML distributed applications through their entire lifecycle on the Edge or Cloud.
Edge ML-Ops enables the automated model management to select the best performing model, whether locally trained or the global aggregate, to address the specific needs of the environment This provides data scientists with a standardised approach to training and scaling new models and keeps their models up to date by triggering model retraining and redeployment.
Unlock the Full Potential of Edge AI with OctaiPipe
It is clear that OctaiPipe is the ultimate Edge AI platform to unlock greater productivity, accelerate AI initiatives, ensure data privacy in machine learning, and reduce costs.
It is the only platform that allows data scientists to put edge AI models in production in minutes without needing any data engineering, software engineering, or networking skills. This makes OctaiPipe the ideal solution for machine learning in large-scale IoT environments where privacy, security, and cost-efficiency are the top priorities.
Get the best out of model deployment and overcome Edge AI challenges by keeping the full spectrum of Federated Learning, Edge ML, and ML-Ops capabilities at your fingertips!
Meet our expert team now to learn how OctaiPipe can help you train, deploy, and manage models in IoT more privately, cost efficiently, and resiliently!
