  Decentralized learning (DL) offers a powerful framework where nodes
collaboratively train models without sharing raw data and without the
coordination of a central server. In the iterative rounds of DL, models are
trained locally, shared with neighbors in the topology, and aggregated with
other models received from neighbors. Sharing and merging models contribute to
convergence towards a consensus model that generalizes better across the
collective data captured at training time. In addition, the energy consumption
while sharing and merging model parameters is negligible compared to the energy
spent during the training phase. Leveraging this fact, we present SkipTrain, a
novel DL algorithm, which minimizes energy consumption in decentralized
learning by strategically skipping some training rounds and substituting them
with synchronization rounds. These training-silent periods, besides saving
energy, also allow models to better mix and finally produce models with
superior accuracy than typical DL algorithms that train at every round. Our
empirical evaluations with 256 nodes demonstrate that SkipTrain reduces energy
consumption by 50% and increases model accuracy by up to 12% compared to
D-PSGD, the conventional DL algorithm.
