Federated Learning for IoT
Artificial intelligence and machine learning, the titan technologies of the 21st century, have been around long before today. The first AI and ML solutions were developed far back in the 1950s, but they were nowhere near as sophisticated as today.
These symbiotic technologies are on a trajectory of continual development, improvement, and sophistication. In just a couple of years, we’ve made progress in the field of AI that has completely changed the way it works, where it’s used, and how we tend to use it.
From chatbots to algorithm-deciphering, AI is everywhere around us, and it’s just getting started. The future isn’t digital – it’s enhanced. The key to this enhancement lies in AI technology.
The Issues AI Faces
One of the more prominent issues holding back the development of AI is inadequate data transaction practices. While other technologies such as big data plan to expand the scope of the data that’s up for processing, these don’t provide the necessary solutions for relatively slow data transfer speeds.
Data for machine learning is stored in isolated and private data centers, in most cases. The goal is not to violate the privacy and rights of others by observing and learning without their consent. Maintaining the data center isn’t as simple as storing it, as the data has to be refined, sequenced, and protected adequately for machine learning applications.
In turn, this manifests in a relatively outdated framework that isn’t doing much in terms of developing the way that AI functions, improving its speed, and increasing its sophistication.
What Is Federated Learning?
Federated learning is the proposed framework that promises to solve most of the issues AI faces today, so AI and ML technologies can be further refined for the future. Through federated learning, programmers can create ML models that utilize datasets that are distributed cross-platform while providing the necessary measures for preventing data leaks.
In layman’s terms, federated learning is a framework that proposes taking the singularity out of the ML process and expanding it across a selection of devices. Since most devices already have sophisticated technology, this theory proposes using it so that the AI has unrivaled access to data.
That is especially important for another technology that is seeing a rise in popularity, which is IoT.
Federated Learning and IoT
IoT stands for the Internet of Things. It’s one of the more contemporary technologies that promises to streamline how we live by optimizing our lives through technology. IoT includes all of the smart devices connected to the internet, creating a loosely connected framework.
Through IoT, these devices are synchronized and co-respondent. IoT applies to physical objects enriched with sensors, software, and, most importantly, an internet connection.
To put this into perspective, IoT is a collection of devices, all of which use the same wireless connection and communicate with each other to make human life that much simpler, easier, and more comfortable.
The use of IoT, while dominant in the field of smart homes, still has a long way to go. The potential for this technology to change our lives is perhaps greater than that of other contemporary technologies.
The same things hold IoT back as AI and ML – the lack of a sophisticated enough data transaction and communication framework.
You could probably tell where this is going – Federated learning is the solution to the issues we face in IoT today and could take the technology to the next level.
What Are the Biggest Challenges to Implementation?
Speed, sophistication, computing power, and storage are the four most significant challenges of federated learning. As federated learning is quite sophisticated, it requires equal sophistication in the tools it uses to streamline AI and ML.
While all solutions to these issues exist and are readily available, there are few data transactions.
Federated learning is a pillar of on-device machine learning, but it does mandate a cloud-based approach. Until most devices are streamlined to work in this manner, federated learning isn’t going to see much real-life application.
The Many Future Use Cases of Federated Learning
The advancement of technology is inevitable, and while it’s marching on by the day, it’s still important to have perspective on what it’s going to bring with it. Federated learning will be the key to the revolution of many industries that have been around for a while, such as:
Automotive Industry
Federated learning has a great application in the automotive industry. As automotive manufacturers are turning towards robotic production, federated learning will have an application in manufacturing. Outside of manufacturing, federated learning can help cars adapt better to their owners’ wants, needs, and requests.
Health Industry
The health industry relies heavily on technology. While nanotechnology is still a fair bit away from implementation, many other modern medical world processes rely on AI. These processes can be optimized and streamlined through federated learning, making them useful for saving human lives.
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Financial Industry
The financial industry is hectic – and it also relies heavily on AI for its algorithm solving capabilities. The financial sector can become much faster, simpler, and more accurate through federated learning and IoT smart solutions.
Final Thoughts
Federated learning is one of the more exciting frameworks that might change IoT, machine learning, and artificial intelligence for the better. A fantastic way to implement these changes is through Pandio Pulsar, as the tool enables lightweight computing, cloud-based data transactions, and smart pipe integration.
Through the use of tools like this, most of the burden is taken away from the processing of information itself, which makes up the bulk of the time consumed in processes such as this. This simulacrum of a streamlined, simple, and enhanced future is now closer than ever before.