  Federated Learning (FL) enables distributed training of machine learning
models while keeping personal data on user devices private. While we witness
increasing applications of FL in the area of mobile sensing, such as human
activity recognition (HAR), FL has not been studied in the context of a
multi-device environment (MDE), wherein each user owns multiple data-producing
devices. With the proliferation of mobile and wearable devices, MDEs are
increasingly becoming popular in ubicomp settings, therefore necessitating the
study of FL in them. FL in MDEs is characterized by being not independent and
identically distributed (non-IID) across clients, complicated by the presence
of both user and device heterogeneities. Further, ensuring efficient
utilization of system resources on FL clients in a MDE remains an important
challenge. In this paper, we propose FLAME, a user-centered FL training
approach to counter statistical and system heterogeneity in MDEs, and bring
consistency in inference performance across devices. FLAME features (i)
user-centered FL training utilizing the time alignment across devices from the
same user; (ii) accuracy- and efficiency-aware device selection; and (iii)
model personalization to devices. We also present an FL evaluation testbed with
realistic energy drain and network bandwidth profiles, and a novel class-based
data partitioning scheme to extend existing HAR datasets to a federated setup.
Our experiment results on three multi-device HAR datasets show that FLAME
outperforms various baselines by 4.3-25.8% higher F1 score, 1.02-2.86x greater
energy efficiency, and up to 2.06x speedup in convergence to target accuracy
through fair distribution of the FL workload.
