  The rapid development of artificial intelligence (AI) over massive
applications including Internet-of-things on cellular network raises the
concern of technical challenges such as privacy, heterogeneity and resource
efficiency.
  Federated learning is an effective way to enable AI over massive distributed
nodes with security.
  However, conventional works mostly focus on learning a single global model
for a unique task across the network, and are generally less competent to
handle multi-task learning (MTL) scenarios with stragglers at the expense of
acceptable computation and communication cost. Meanwhile, it is challenging to
ensure the privacy while maintain a coupled multi-task learning across multiple
base stations (BSs) and terminals. In this paper, inspired by the natural
cloud-BS-terminal hierarchy of cellular works, we provide a viable
resource-aware hierarchical federated MTL (RHFedMTL) solution to meet the
heterogeneity of tasks, by solving different tasks within the BSs and
aggregating the multi-task result in the cloud without compromising the
privacy. Specifically, a primal-dual method has been leveraged to effectively
transform the coupled MTL into some local optimization sub-problems within BSs.
Furthermore, compared with existing methods to reduce resource cost by simply
changing the aggregation frequency,
  we dive into the intricate relationship between resource consumption and
learning accuracy, and develop a resource-aware learning strategy for local
terminals and BSs to meet the resource budget. Extensive simulation results
demonstrate the effectiveness and superiority of RHFedMTL in terms of improving
the learning accuracy and boosting the convergence rate.
