  The conjunction of edge intelligence and the ever-growing Internet-of-Things
(IoT) network heralds a new era of collaborative machine learning, with
federated learning (FL) emerging as the most prominent paradigm. With the
growing interest in these learning schemes, researchers started addressing some
of their most fundamental limitations. Indeed, conventional FL with a central
aggregator presents a single point of failure and a network bottleneck. To
bypass this issue, decentralized FL where nodes collaborate in a peer-to-peer
network has been proposed. Despite the latter's efficiency, communication costs
and data heterogeneity remain key challenges in decentralized FL. In this
context, we propose a novel scheme, called opportunistic
communication-efficient decentralized federated learning, a.k.a., OCD-FL,
consisting of a systematic FL peer selection for collaboration, aiming to
achieve maximum FL knowledge gain while reducing energy consumption.
Experimental results demonstrate the capability of OCD-FL to achieve similar or
better performances than the fully collaborative FL, while significantly
reducing consumed energy by at least 30% and up to 80%.
