Federated learning: a comprehensive review of recent advances and applications

Federated Learning is a promising technique for preserving data privacy that enables communication between distributed nodes without the need for a central server. Previously, data privacy concerns have made it challenging for firms to share large datasets in critical locations, as network data tampering is a potential risk. Federated Learning offers a solution by allowing the benefits of data privacy without the need for data to be shared with a central server. This field has attracted researchers from a range of disciplines and is still in its early stages. This systematic review article provides an overview of Federated Learning, covering its framework, categories, and benefits, as well as various application areas. The article also highlights recent cutting-edge research and addresses fundamental concerns that have emerged from this research, such as the need for more advanced privacy preservation techniques. Finally, the article suggests future research directions to facilitate the practical application of Federated Learning in real-world scenarios. As Federated Learning is a relatively new research field, there is still much to be explored. However, its potential advantages in terms of data privacy and collaboration make it a compelling area of study for researchers and businesses alike. The article serves as a valuable resource for those interested in understanding the current state of Federated Learning and its potential applications.