  With the advent of interconnected and sensor-equipped edge devices, Federated
Learning (FL) has gained significant attention, enabling decentralized learning
while maintaining data privacy. However, FL faces two challenges in real-world
tasks: expensive data labeling and domain shift between source and target
samples. In this paper, we introduce a privacy-preserving, resource-efficient
FL concept for client adaptation in hardware-constrained environments. Our
approach includes server model pre-training on source data and subsequent
fine-tuning on target data via low-end clients. The local client adaptation
process is streamlined by probabilistic mixing of instance-level feature
statistics approximated from source and target domain data. The adapted
parameters are transferred back to the central server and globally aggregated.
Preliminary results indicate that our method reduces computational and
transmission costs while maintaining competitive performance on downstream
tasks.
