  Many of the machine learning (ML) tasks are focused on centralized learning
(CL), which requires the transmission of local datasets from the clients to a
parameter server (PS) leading to a huge communication overhead. Federated
learning (FL) overcomes this issue by allowing the clients to send only the
model updates to the PS instead of the whole dataset. In this way, FL brings
the learning to edge level, wherein powerful computational resources are
required on the client side. This requirement may not always be satisfied
because of diverse computational capabilities of edge devices. We address this
through a novel hybrid federated and centralized learning (HFCL) framework to
effectively train a learning model by exploiting the computational capability
of the clients. In HFCL, only the clients who have sufficient resources employ
FL; the remaining clients resort to CL by transmitting their local dataset to
PS. This allows all the clients to collaborate on the learning process
regardless of their computational resources. We also propose a sequential data
transmission approach with HFCL (HFCL-SDT) to reduce the training duration. The
proposed HFCL frameworks outperform previously proposed non-hybrid FL (CL)
based schemes in terms of learning accuracy (communication overhead) since all
the clients collaborate on the learning process with their datasets regardless
of their computational resources.
