Blockchain, smart contracts and federated learning technologies have revolutionized the way transactions are conducted and data is protected. However, many people may find it difficult to understand these concepts. For this reason, this article will explain one of these technologies in a simple way using an example.
Federated Learning technology has become an essential tool for protecting data privacy in the digital age. This approach allows users to maintain control over their own data, without the need to share it with a central server. In this article, we will explain the problem of not using Federated Learning at a general level, through a simple but effective example.
Let’s imagine that we are a shepherd, and we have a flock of sheep. We can feed them in two ways: collect all the feed and bring it to a single field, or distribute the feeding sites and allow the sheep to graze in different fields. This example illustrates how a centralized approach to data can be detrimental and costly.
If we focus on a single location to collect and accumulate data, as would be the case with bringing all feed to a single pasture, we can face a number of unnecessary risks. For example, data collection and transfer may be more vulnerable and, therefore, the central server may be less secure. In addition, once transferred, data may be trapped due to privacy clauses and other regulations. In the worst-case scenario, if the central server suffers an attack, failure or error, we will have to move all the data to another location, which can result in high costs and serious damage to the platform.
On the other hand, if we use the Federated Learning approach, we can protect the data and avoid these risks. By not focusing on a central server, the customer’s data, in this case the feed, does not have to leave the pasture where it is located. This avoids transportation and accumulation costs, as well as the possibility of damage to the accumulated data. Even if any server or device fails, we will still have access to the remaining data to continue feeding and working on the platform.
In conclusion, the use of Federated Learning is critical to protecting data privacy and avoiding the risks associated with a centralized approach. By allowing users to maintain control over their own data, a more secure, efficient and cost-effective solution is achieved.
References:
YouTube video on the GREEN channel
