The goal of artificial intelligence is to replace the algorithms programmed explicitly by humans with self-learning algorithms. The algorithms undergo training from the user’s data and then a phase of inference wherein it processes the real-world data and comes up with a prediction. In order to train an AI, a large volume of data is required which again needs high computing power. Hence AI was built on a centralized network. The user’s data is collected and sent to the cloud where the training and inference of algorithms take place.
Though centralized AI is by far the most common approach, there are many issues that plague this approach. Constantly moving colossal amounts of data to the cloud can create some serious constraints.
One serious concern is that of diminished privacy. The need to transfer user’s data and store it on remote servers can create opportunities for hackers to intercept the data and use it to their advantage.
Many sectors, for instance, bank and military, aren’t ready to share their data and have it stored in the cloud. Hence they cannot reap the benefits of centralized AI.
In cases where AI has to interact with real-world in real-time such as in autonomous cars, centralized AI may not be helpful.
High transfer costs involved in moving colossal volumes of data is another constraint of centralized AI.
The emergence of Federated Learning plays a crucial role in overcoming some of these constraints. It is the brainchild of Google which forms the basis of collaborated, yet confidential AI.
What is Federated Learning?
Federated learning is a machine learning technique that allows the AI models on your mobile phone to learn directly from the user’s data without having to send the same to the cloud for training and inference of AI.
In the centralized approach, the AI model on the user’s phone doesn’t improve itself by learning from the user directly. Instead, the user’s input is sent to the cloud where the AI model lives. This data is clubbed with data from other users to train the AI and the improved version is pushed down to the user’s phone as updates.
In a way, the AI model in the user’s phone doesn’t learn from the way the user uses the app but by the way everyone uses the app.
How does Federated Learning Work?
A central algorithm is downloaded on every phone from the cloud. This algorithm is trained continuously from the way the user uses the app making the algorithm local and personalized as per the user’s choices.
The new learnings are then sent as an update to the cloud through encryption which means only the new findings are sent to the cloud leaving behind the personal data.
The new updates are incorporated into the central algorithm and this trained new central model is sent as an update to the user’s mobile phone where it is integrated with the local model.
Benefits of Federated Learning
- Personal data never leaves the user’s phone and only the new discoveries are sent as updates in an encrypted manner thus making it impossible for the hackers to intercept the data.
- Federated learning is much cheaper and more convenient as the workload needed is lower due to lighter updates.
- Allows for real-time inferences without any latency problems.
Here at SMACAR Solutions, we work on cutting-edge technologies such as augmented reality, virtual reality, and IoT. If you are looking to get started on these technologies, then look no further. Schedule a consultation now to talk to our experts.