  Federated Learning (FL) has garnered significant interest recently due to its
potential as an effective solution for tackling many challenges in diverse
application scenarios, for example, data privacy in network edge traffic
classification. Despite its recognized advantages, FL encounters obstacles
linked to statistical data heterogeneity and labeled data scarcity during the
training of single-task models for machine learning-based traffic
classification, leading to hindered learning performance. In response to these
challenges, adopting a hard-parameter sharing multi-task learning model with
auxiliary tasks proves to be a suitable approach. Such a model has the
capability to reduce communication and computation costs, navigate statistical
complexities inherent in FL contexts, and overcome labeled data scarcity by
leveraging knowledge derived from interconnected auxiliary tasks. This paper
introduces a new framework for federated auxiliary hard-parameter sharing
multi-task learning, namely, FedAuxHMTL. The introduced framework incorporates
model parameter exchanges between edge server and base stations, enabling base
stations from distributed areas to participate in the FedAuxHMTL process and
enhance the learning performance of the main task-network edge traffic
classification. Empirical experiments are conducted to validate and demonstrate
the FedAuxHMTL's effectiveness in terms of accuracy, total global loss,
communication costs, computing time, and energy consumption compared to its
counterparts.
