  Fully distributed learning schemes such as Gossip Learning (GL) are gaining
momentum due to their scalability and effectiveness even in dynamic settings.
However, they often imply a high utilization of communication and computing
resources, whose energy footprint may jeopardize the learning process,
particularly on battery-operated IoT devices. To address this issue, we present
Optimized Gossip Learning (OGL)}, a distributed training approach based on the
combination of GL with adaptive optimization of the learning process, which
allows for achieving a target accuracy while minimizing the energy consumption
of the learning process. We propose a data-driven approach to OGL management
that relies on optimizing in real-time for each node the number of training
epochs and the choice of which model to exchange with neighbors based on
patterns of node contacts, models' quality, and available resources at each
node. Our approach employs a DNN model for dynamic tuning of the aforementioned
parameters, trained by an infrastructure-based orchestrator function. We
performed our assessments on two different datasets, leveraging time-varying
random graphs and a measurement-based dynamic urban scenario. Results suggest
that our approach is highly efficient and effective in a broad spectrum of
network scenarios.
