Federated learning is a distributed machine learning approach that allows models to be trained on decentralized data sources while preserving data privacy. This technique is particularly useful for applications such as IoT and edge computing, where data is generated by a large number of devices in different locations. Federated learning enables real-time decision-making and more efficient data processing, making it an important tool in the era of big data and IoT.
Decentralized Applications (DApps) are computer applications that run on a blockchain network, allowing for greater security, transparency, and autonomy than traditional centralized applications. DApps can be used for a variety of purposes, from financial transactions and gaming to social media and governance, and are built using smart contracts and consensus algorithms.
Web3 is the next generation of the internet, designed to be more transparent, decentralized, and secure than its predecessors. Unlike Web1 and Web2, which were characterized by centralized control and limited user interaction, Web3 is built on blockchain technology and other decentralized technologies, such as smart contracts and decentralized applications.
