What is Federated Learning?

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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.

Other Definitions

Graph Neural Networks are machine learning models designed to handle data structured as graphs. They can capture relationships and dependencies between entities and perform…
Modular Neural Networks are AI models composed of smaller interconnected modules, each responsible for a specific sub-task or component. These modular architectures allow for…
Adversarial machine learning involves studying and defending AI models against attacks or adversarial examples designed to deceive the system. By understanding vulnerabilities and deploying…
Cognitive Computing involves creating computer systems that can imitate human cognitive abilities, such as perception, reasoning, learning, and problem-solving. By combining AI, Machine Learning,…