  Today, various machine learning (ML) applications offer continuous data
processing and real-time data analytics at the edge of a wireless network.
Distributed real-time ML solutions are highly sensitive to the so-called
straggler effect caused by resource heterogeneity and alleviated by various
computation offloading mechanisms that seriously challenge the communication
efficiency, especially in large-scale scenarios. To decrease the communication
overhead, we rely on device-to-device (D2D) connectivity that improves spectrum
utilization and allows efficient data exchange between devices in proximity. In
particular, we design a novel D2D-aided coded federated learning method
(D2D-CFL) for efficient load balancing across devices. The proposed solution
captures system dynamics, including data (time-dependent learning model, varied
intensity of data arrivals), device (diverse computational resources and volume
of training data), and deployment (varied locations and D2D graph
connectivity). To minimize the number of communication rounds, we derive an
optimal compression rate for achieving minimum processing time and establish
its connection with the convergence time. The resulting optimization problem
provides suboptimal compression parameters, which improve the total training
time. Our proposed method is beneficial for real-time collaborative
applications, where the users continuously generate training data resulting in
the model drift.
