  Human activity recognition (HAR) based on mobile sensors plays an important
role in ubiquitous computing. However, the rise of data regulatory constraints
precludes collecting private and labeled signal data from personal devices at
scale. Federated learning has emerged as a decentralized alternative solution
to model training, which iteratively aggregates locally updated models into a
shared global model, therefore being able to leverage decentralized, private
data without central collection. However, the effectiveness of federated
learning for HAR is affected by the fact that each user has different activity
types and even a different signal distribution for the same activity type.
Furthermore, it is uncertain if a single global model trained can generalize
well to individual users or new users with heterogeneous data. In this paper,
we propose Meta-HAR, a federated representation learning framework, in which a
signal embedding network is meta-learned in a federated manner, while the
learned signal representations are further fed into a personalized
classification network at each user for activity prediction. In order to boost
the representation ability of the embedding network, we treat the HAR problem
at each user as a different task and train the shared embedding network through
a Model-Agnostic Meta-learning framework, such that the embedding network can
generalize to any individual user. Personalization is further achieved on top
of the robustly learned representations in an adaptation procedure. We
conducted extensive experiments based on two publicly available HAR datasets as
well as a newly created HAR dataset. Results verify that Meta-HAR is effective
at maintaining high test accuracies for individual users, including new users,
and significantly outperforms several baselines, including Federated Averaging,
Reptile and even centralized learning in certain cases.
