Federated Learning is a privacy-preserving technique where AI models are trained across multiple decentralised devices or systems without sharing raw data. Instead, only aggregated model updates are exchanged, ensuring data privacy and security. Federated Learning enables businesses to harness the collective intelligence of distributed devices while maintaining data confidentiality.
Data Preprocessing involves preparing and cleaning raw data before analysis. By removing noise, selecting relevant features, and addressing missing values, businesses can ensure data…