  The fast development of Internet-of-Things (IoT) devices and applications has
led to vast data collection, potentially containing irrelevant, noisy, or
redundant features that degrade learning model performance. These collected
data can be processed on either end-user devices (clients) or edge/cloud
server. Feature construction is a pre-processing technique that can generate
discriminative features and reveal hidden relationships between original
features within a dataset, leading to improved performance and reduced
computational complexity of learning models. Moreover, the communication cost
between clients and edge/cloud server can be minimized in situations where a
dataset needs to be transmitted for further processing. In this paper, the
first federated feature construction (FFC) method called multimodal multiple
FFC (MMFFC) is proposed by using multimodal optimization and gravitational
search programming algorithm. This is a collaborative method for constructing
multiple high-level features without sharing clients' datasets to enhance the
trade-off between accuracy of the trained model and overall communication cost
of the system, while also reducing computational complexity of the learning
model. We analyze and compare the accuracy-cost trade-off of two scenarios,
namely, 1) MMFFC federated learning (FL), using vanilla FL with pre-processed
datasets on clients and 2) MMFFC centralized learning, transferring
pre-processed datasets to an edge server and using centralized learning model.
The results on three datasets for the first scenario and eight datasets for the
second one demonstrate that the proposed method can reduce the size of datasets
for about 60\(\%\), thereby reducing communication cost and improving accuracy
of the learning models tested on almost all datasets.
