#FL

What is Federated learning (FL) FL aims to train NN via utilising locally trained models and aggregating a global model. This can be done via a variety of networking paradigms most notably centralised and decentralised aggregation. Setup and Initialisation: Establish a central server (if centralised) and multiple client devices or nodes. Define the machine learning model architecture and hyperparameter. Data Partitioning: Each client retains control of its data locally, ensuring data privacy.

What is PET-Streaming The aim of this project is to provide privacy enchanging technology. Current goals are Input Take a stream of cloud data Process Anonymise the data Check if the data within the stream and does appropiate validation - (Does not store) (Catalogue - is verifiied against a list of data assets and data controls) Output Send the anonymise data to the end user. Usecase The aim is to be able to to take PII data, and provide an api to the dataowner to enable the utilisation of the data by any organisation that they deem fit, without having to worry about the misuse of PII and the end user can ensure the data integrity