Abstract
Federated learning is a technique that trains the knowledge across different decentralized devices holding samples of information without exchanging them. The concept is additionally called collaborative learning. In federated learning, the clients are allowed separately to teach the deep neural network models with the local data combined at the deep neural network model at the central server. All the local datasets are uploaded to a minimum of one server, so it assumes that local data samples are identically distributed. It doesn’t transmit the information to the server. Because of its security and privacy concerns, it’s widely utilized in many applications like IoT, cloud computing; Edge computing, Vehicular edge computing, and many more. The details of implementation for the privacy of information in federated learning for shielding the privacy of local uploaded data are described. Since there will be trillions of edge devices, the system efficiency and privacy should be taken with no consideration in evaluating federated learning algorithms in computing technologies. This will incorporate the effectiveness, privacy, and usage of federated learning in several computing technologies. Here, different applications of federated learning, its privacy concerns, and its definition in various fields of computing like IoT, Edge, and Cloud Computing are presented.
