Handbook on Federated Learning Advances, Applications and Opportunities

"Mobile, wearable, and self-driving telephones are just a few examples of modern distributed networks that generate enormous amount of information every day. Due to the growing computing capacity of these devices as well as concerns over the transfer of private information, it is becoming increasingly attractive to store data locally and to move network computing to the border of devices. Federated learning has developed as a model of education in these situations. Federated Learning (FL) is an expert form of decentralized machine learning (ML). Federated learning is essential in areas like privacy, large-scale machine education and distribution. It is also based on the current stage of ICT and new hardware technology and is the next generation of artificial intelligence (AI). In 2015, AI has attracted new interest from around the world after the Alpha Go upset of professional Go players. In the Federated Learning, central Machine Learning model is built with all the data available in a centralised environment in the traditional machine learning. It works without problems when the predictions can be served by a central server. Users require fast responses in mobile computing but the time between user and server can be too slow to have a good user experience. The model can be placed in the end-user device, but continuous learning is a challenge to overcome, as models are trained in a complete dataset and the end-user device lacks access to the entire data package. Another challenge with traditional machine learning is that user data is aggregated at a central location for training in machine learning, which can conflict with certain countries' privacy policies and make the data more vulnerable to data violation. This book provides complete comprehensive approach in federated learning in various aspects"--