  We consider the problem of learning the energy disaggregation signals for
residential load data. Such task is referred as non-intrusive load monitoring
(NILM), and in order to find individual devices' power consumption profiles
based on aggregated meter measurements, a machine learning model is usually
trained based on large amount of training data coming from a number of
residential homes. Yet collecting such residential load datasets require both
huge efforts and customers' approval on sharing metering data, while load data
coming from different regions or electricity users may exhibit heterogeneous
usage patterns. Both practical concerns make training a single, centralized
NILM model challenging. In this paper, we propose a decentralized and
task-adaptive learning scheme for NILM tasks, where nested meta learning and
federated learning steps are designed for learning task-specific models
collectively. Simulation results on benchmark dataset validate proposed
algorithm's performance on efficiently inferring appliance-level consumption
for a variety of homes and appliances.
