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Nowadays, billions of phones, IoT and edge devices around the world generatedata continuously, enabling many Machine Learning (ML)-based products andapplications. However, due to increasing privacy concerns and regulations,these data tend to reside on devices (clients) instead of being centralized forperforming traditional ML model training. Federated Learning (FL) is adistributed approach in which a single server and multiple clientscollaboratively build an ML model without moving data away from clients.Whereas existing studies on FL have their own experimental evaluations, mostexperiments were conducted using a simulation setting or a small-scale testbed.This might limit the understanding of FL implementation in realisticenvironments. In this empirical study, we systematically conduct extensiveexperiments on a large network of IoT and edge devices (called IoT-Edgedevices) to present FL real-world characteristics, including learningperformance and operation (computation and communication) costs. Moreover, wemainly concentrate on heterogeneous scenarios, which is the most challengingissue of FL. By investigating the feasibility of on-device implementation, ourstudy provides valuable insights for researchers and practitioners, promotingthe practicality of FL and assisting in improving the current design of real FLsystems.

An Empirical Study of Federated Learning on IoT-Edge Devices: Resource Allocation and Heterogeneity