TechDispatch

Federated Learning (FL) presents a promising approach to machine learning (ML) by allowing multiple sources of data (devices or entities) to collaboratively train a shared model while keeping data decentralised. This approach mitigates privacy risks as raw data remains locally on the sources, which is particularly beneficial in scenarios where data sensitivity or regulatory requirements make data centralisation[1] impractical. Applications of FL are diverse, spanning personalised recommendations, healthcare data analysis, data spaces, and autonomous transport systems, where ensuring privacy and data protection is paramount.

The brain, together with the spinal cord, constitute the central nervous system, which plays a crucial role in regulating and coordinating various bodily functions including human cognitive capabilities. The intricate and peculiar activity of the brain has always been a field of study considered particularly interesting.

The adoption of artificial intelligence (AI) is rapidly growing in sectors such as healthcare, finance, transportation, manufacturing and entertainment. Its increasing popularity in recent years is largely due to its ability to automate tasks, such as processing large amounts of information or identifying patterns, and its widespread availability to the public.

Countries around the world are examining whether they should offer central bank money to the public not only as banknotes and coins, but also in digital form.
The fact that the majority of central banks around the world have already started exploring the possibility of launching a state-owned digital currency comes as a response to the increased adoption of digital, contactless payments, cryptocurrencies and e-commerce, further accelerated by Covid-19, b and also due to the possibilities offered by these digital currencies as a more flexible monetary tool compared to the existing non-digital currency.
Privacy, the protection of personal data and security are amongst the most important requirements, which impact the core design choices to be taken, as well as expected citizens’ trust and acceptance.

The Fediverse consists of many independent, interoperable social media platforms that allow their users to interact with each other across those platforms. In the absence of vendor lock-in, users can choose a platform with terms and data location matching their preferences.