  In the realm of ubiquitous computing, Human Activity Recognition (HAR) is
vital for the automation and intelligent identification of human actions
through data from diverse sensors. However, traditional machine learning
approaches by aggregating data on a central server and centralized processing
are memory-intensive and raise privacy concerns. Federated Learning (FL) has
emerged as a solution by training a global model collaboratively across
multiple devices by exchanging their local model parameters instead of local
data. However, in realistic settings, sensor data on devices is
non-independently and identically distributed (Non-IID). This means that data
activity recorded by most devices is sparse, and sensor data distribution for
each client may be inconsistent. As a result, typical FL frameworks in
heterogeneous environments suffer from slow convergence and poor performance
due to deviation of the global model's objective from the global objective.
Most FL methods applied to HAR are either designed for overly ideal scenarios
without considering the Non-IID problem or present privacy and scalability
concerns. This work addresses these challenges, proposing CDFL, an efficient
federated learning framework for image-based HAR. CDFL efficiently selects a
representative set of privacy-preserved images using contrastive learning and
deep clustering, reduces communication overhead by selecting effective clients
for global model updates, and improves global model quality by training on
privacy-preserved data. Our comprehensive experiments carried out on three
public datasets, namely Stanford40, PPMI, and VOC2012, demonstrate the
superiority of CDFL in terms of performance, convergence rate, and bandwidth
usage compared to state-of-the-art approaches.
