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.
Ensemble Learning involves combining multiple Machine Learning models to achieve superior performance and accuracy. By leveraging the “wisdom of the crowd,” Ensemble Learning mitigates…