Description: Viral propagation refers to the rapid dissemination of information or models across a network, leveraging connectivity and interaction among nodes. This phenomenon is based on the idea that, much like a biological virus, information can replicate and spread exponentially. In the context of distributed computing and collaborative learning, viral propagation becomes a crucial mechanism for updating and improving machine learning models without the need to centralize data. Each node in the network, which can be a device or server, contributes to the process by training models locally and sharing only the updated parameters, rather than the data itself. This not only preserves data privacy but also allows the model to benefit from a diversity of distributed data. Viral propagation in this context is essential for optimizing collaborative learning, where multiple entities can contribute to the development of a robust and accurate model while minimizing the risks associated with data centralization. This approach has become increasingly relevant in a world where data privacy and security are paramount concerns.
