Federated Learning on Mobile Devices: Challenges, Opportunities, and Future Directions

Federated Learning (FL) is a decentralized machine learning paradigm that enables model training across distributed devices while preserving data privacy. With the proliferation of mobile devices and the increasing demand for privacy-preserving AI, FL has emerged as a promising solution for training models on edge devices. This paper explores the implementation of Federated Learning on mobile devices, highlighting the technical challenges, opportunities, and future directions. By addressing issues such as resource constraints, communication overhead, and heterogeneity, FL can unlock the potential of collaborative learning on mobile platforms while ensuring data privacy and security. Keywords Mobile applications, accessibility, disabilities, user interface, android, ios.