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The rapid development of artificial intelligence (AI) over massiveapplications including Internet-of-things on cellular network raises theconcern of technical challenges such as privacy, heterogeneity and resourceefficiency.  Federated learning is an effective way to enable AI over massive distributednodes with security.  However, conventional works mostly focus on learning a single global modelfor a unique task across the network, and are generally less competent tohandle multi-task learning (MTL) scenarios with stragglers at the expense ofacceptable computation and communication cost. Meanwhile, it is challenging toensure the privacy while maintain a coupled multi-task learning across multiplebase stations (BSs) and terminals. In this paper, inspired by the naturalcloud-BS-terminal hierarchy of cellular works, we provide a viableresource-aware hierarchical federated MTL (RHFedMTL) solution to meet theheterogeneity of tasks, by solving different tasks within the BSs andaggregating the multi-task result in the cloud without compromising theprivacy. Specifically, a primal-dual method has been leveraged to effectivelytransform the coupled MTL into some local optimization sub-problems within BSs.Furthermore, compared with existing methods to reduce resource cost by simplychanging the aggregation frequency,  we dive into the intricate relationship between resource consumption andlearning accuracy, and develop a resource-aware learning strategy for localterminals and BSs to meet the resource budget. Extensive simulation resultsdemonstrate the effectiveness and superiority of RHFedMTL in terms of improvingthe learning accuracy and boosting the convergence rate.

RHFedMTL: Resource-Aware Hierarchical Federated Multi-Task Learning