  Smartphones, autonomous vehicles, and the Internet-of-things (IoT) devices
are considered the primary data source for a distributed network. Due to a
revolutionary breakthrough in internet availability and continuous improvement
of the IoT devices capabilities, it is desirable to store data locally and
perform computation at the edge, as opposed to share all local information with
a centralized computation agent. A recently proposed Machine Learning (ML)
algorithm called Federated Learning (FL) paves the path towards preserving data
privacy, performing distributed learning, and reducing communication overhead
in large-scale machine learning (ML) problems. This paper proposes an FL model
by monitoring client activities and leveraging available local computing
resources, particularly for resource-constrained IoT devices (e.g., mobile
robots), to accelerate the learning process. We assign a trust score to each FL
client, which is updated based on the client's activities. We consider a
distributed mobile robot as an FL client with resource limitations either in
memory, bandwidth, processor, or battery life. We consider such mobile robots
as FL clients to understand their resource-constrained behavior in a real-world
setting. We consider an FL client to be untrustworthy if the client infuses
incorrect models or repeatedly gives slow responses during the FL process.
After disregarding the ineffective and unreliable client, we perform local
training on the selected FL clients. To further reduce the straggler issue, we
enable an asynchronous FL mechanism by performing aggregation on the FL server
without waiting for a long period to receive a particular client's response.
