  Federated learning struggles with their heavy energy footprint on
battery-powered devices. The learning process keeps all devices awake while
draining expensive battery power to train a shared model collaboratively, yet
it may still leak sensitive personal information. Traditional energy management
techniques in system kernel mode can force the training device entering low
power states, but it may violate the SLO of the collaborative learning. To
address the conflict between learning SLO and energy efficiency, we propose
DEAL, an energy efficient learning system that saves energy and preserves
privacy with a decremental learning design. DEAL reduces the energy footprint
from two layers: 1) an optimization layer that selects a subset of workers with
sufficient capacity and maximum rewards. 2) a specified decremental learning
algorithm that actively provides a decremental and incremental update
functions, which allows kernel to correctly tune the local DVFS. We prototyped
DEAL in containerized services with modern smartphone profiles and evaluated it
with several learning benchmarks with realistic traces. We observed that DEAL
achieves 75.6%-82.4% less energy footprint in different datasets, compared to
the traditional methods. All learning processes are faster than
state-of-the-practice FL frameworks up to 2-4X in model convergence.
