  Wireless traffic prediction is essential for cellular networks to realize
intelligent network operations, such as load-aware resource management and
predictive control. Existing prediction approaches usually adopt centralized
training architectures and require the transferring of huge amounts of traffic
data, which may raise delay and privacy concerns for certain scenarios. In this
work, we propose a novel wireless traffic prediction framework named
\textit{Dual Attention-Based Federated Learning} (FedDA), by which a
high-quality prediction model is trained collaboratively by multiple edge
clients. To simultaneously capture the various wireless traffic patterns and
keep raw data locally, FedDA first groups the clients into different clusters
by using a small augmentation dataset. Then, a quasi-global model is trained
and shared among clients as prior knowledge, aiming to solve the statistical
heterogeneity challenge confronted with federated learning. To construct the
global model, a dual attention scheme is further proposed by aggregating the
intra- and inter-cluster models, instead of simply averaging the weights of
local models. We conduct extensive experiments on two real-world wireless
traffic datasets and results show that FedDA outperforms state-of-the-art
methods. The average mean squared error performance gains on the two datasets
are up to 10\% and 30\%, respectively.
