  Motion sensors integrated into wearable and mobile devices provide valuable
information about the device users. Machine learning and, recently, deep
learning techniques have been used to characterize sensor data. Mostly, a
single task, such as recognition of activities, is targeted, and the data is
processed centrally at a server or in a cloud environment. However, the same
sensor data can be utilized for multiple tasks and distributed machine-learning
techniques can be used without the requirement of the transmission of data to a
centre. This paper explores Federated Transfer Learning in a Multi-Task manner
for both sensor-based human activity recognition and device position
identification tasks. The OpenHAR framework is used to train the models, which
contains ten smaller datasets. The aim is to obtain model(s) applicable for
both tasks in different datasets, which may include only some label types.
Multiple experiments are carried in the Flower federated learning environment
using the DeepConvLSTM architecture. Results are presented for federated and
centralized versions under different parameters and restrictions. By utilizing
transfer learning and training a task-specific and personalized federated
model, we obtained a similar accuracy with training each client individually
and higher accuracy than a fully centralized approach.
