  The number of Internet of Things (IoT) applications, especially
latency-sensitive ones, have been significantly increased. So, Cloud computing,
as one of the main enablers of the IoT that offers centralized services, cannot
solely satisfy the requirements of IoT applications. Edge/Fog computing, as a
distributed computing paradigm, processes, and stores IoT data at the edge of
the network, offering low latency, reduced network traffic, and higher
bandwidth. The Edge/Fog resources are often less powerful compared to Cloud,
and IoT data is dispersed among many geo-distributed servers. Hence, Federated
Learning (FL), which is a machine learning approach that enables multiple
distributed servers to collaborate on building models without exchanging the
raw data, is well-suited to Edge/Fog computing environments, where data privacy
is of paramount importance. Besides, to manage different FL tasks on Edge/Fog
computing environments, a lightweight resource management framework is required
to manage different incoming FL tasks while does not incur significant overhead
on the system. Accordingly, in this paper, we propose a lightweight FL
framework, called FLight, to be deployed on a diverse range of devices, ranging
from resource limited Edge/Fog devices to powerful Cloud servers. FLight is
implemented based on the FogBus2 framework, which is a containerized
distributed resource management framework. Moreover, FLight integrates both
synchronous and asynchronous models of FL. Besides, we propose a lightweight
heuristic-based worker selection algorithm to select a suitable set of
available workers to participate in the training step to obtain higher training
time efficiency. The obtained results demonstrate the efficiency of the FLight.
