  The provision of communication services via portable and mobile devices, such
as aerial base stations, is a crucial concept to be realized in 5G/6G networks.
Conventionally, IoT/edge devices need to transmit the data directly to the base
station for training the model using machine learning techniques. The data
transmission introduces privacy issues that might lead to security concerns and
monetary losses. Recently, Federated learning was proposed to partially solve
privacy issues via model-sharing with base station. However, the centralized
nature of federated learning only allow the devices within the vicinity of base
stations to share the trained models. Furthermore, the long-range communication
compels the devices to increase transmission power, which raises the energy
efficiency concerns. In this work, we propose distributed federated learning
(DBFL) framework that overcomes the connectivity and energy efficiency issues
for distant devices. The DBFL framework is compatible with mobile edge
computing architecture that connects the devices in a distributed manner using
clustering protocols. Experimental results show that the framework increases
the classification performance by 7.4\% in comparison to conventional federated
learning while reducing the energy consumption.
