  Federated Learning (FL) plays a critical role in distributed systems. In
these systems, data privacy and confidentiality hold paramount importance,
particularly within edge-based data processing systems such as IoT devices
deployed in smart homes. FL emerges as a privacy-enforcing sub-domain of
machine learning that enables model training on client devices, eliminating the
necessity to share private data with a central server. While existing research
has predominantly addressed challenges pertaining to data heterogeneity, there
remains a current gap in addressing issues such as varying device capabilities
and efficient communication. These unaddressed issues raise a number of
implications in resource-constrained environments. In particular, the practical
implementation of FL-based IoT or edge systems is extremely inefficient. In
this paper, we propose "Resource-Efficient Federated Training Framework for
Heterogeneous and Resource-Constrained Environments (REFT)," a novel approach
specifically devised to address these challenges in resource-limited devices.
Our proposed method uses Variable Pruning to optimize resource utilization by
adapting pruning strategies to the computational capabilities of each client.
Furthermore, our proposed REFT technique employs knowledge distillation to
minimize the need for continuous bidirectional client-server communication.
This achieves a significant reduction in communication bandwidth, thereby
enhancing the overall resource efficiency. We conduct experiments for an image
classification task, and the results demonstrate the effectiveness of our
approach in resource-limited settings. Our technique not only preserves data
privacy and performance standards but also accommodates heterogeneous model
architectures, facilitating the participation of a broader array of diverse
client devices in the training process, all while consuming minimal bandwidth.
