  The exponential growth of distributed energy resources is enabling the
transformation of traditional consumers in the smart grid into prosumers. Such
transition presents a promising opportunity for sustainable energy trading.
Yet, the integration of prosumers in the energy market imposes new
considerations in designing unified and sustainable frameworks for efficient
use of the power and communication infrastructure. Furthermore, several issues
need to be tackled to adequately promote the adoption of decentralized
renewable-oriented systems, such as communication overhead, data privacy,
scalability, and sustainability.
  In this article, we present the different aspects and challenges to be
addressed for building efficient energy trading markets in relation to
communication and smart decision-making. Accordingly, we propose a multi-level
pro-decision framework for prosumer communities to achieve collective goals.
Since the individual decisions of prosumers are mainly driven by individual
self-sufficiency goals, the framework prioritizes the individual prosumers'
decisions and relies on the 5G wireless network for fast coordination among
community members. In fact, each prosumer predicts energy production and
consumption to make proactive trading decisions as a response to
collective-level requests. Moreover, the collaboration of the community is
further extended by including the collaborative training of prediction models
using Federated Learning, assisted by edge servers and prosumer home-area
equipment. In addition to preserving prosumers' privacy, we show through
evaluations that training prediction models using Federated Learning yields
high accuracy for different energy resources while reducing the communication
overhead.
