A Time-Continuous Federated Learning Framework for Enabling Intelligent Applications Over Latency-Critical Aerial Networks

Distributed Machine Learning (DML) methods are expected to play a crucial role in the forthcoming 6G era, with the goal of enabling ubiquitous connected intelligence. Distributed intelligence enabled through distributed computing environments, 6G technology, and big data can be extremely supportive of achieving the goals of emerging intelligent IoT applications in the proximity of end users. With this in mind, we propose an advanced Federated Learning (FL) approach for efficiently enabling intelligent applications over latency-critical networks in Non-Terrestrial environments. In the proposed solution, the client and server nodes reduce idle time using a parallel processing approach with the help of a replica of the training model. Next, the proposed FL framework is tested in a Python environment to show its effectiveness with respect to the traditional FL approach.