Distributed Edge Learning with Inter-Type and Intra-Type Over-the-Air Collaboration
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Federated learning (FL) has been widely utilized to leverage the distributed dataset and processing capability of local sensors while preserving data privacy. Considering the utilization of multiple groups of sensors with diverse sensing functions in IoT wireless networks, we focus on a new hybrid data partitioning scenario, where each sensor can only obtain partial data samples on type-specific feature space. This results in the combined sample parallelism among same-type sensors and feature parallelism among different types of sensors, which brings challenges to designing scalable and communication-efficient training algorithms. Different from the conventional FL settings, we transform the training problem to the primal-dual domain and propose a novel hierarchical FL framework where both intra-type and inter-type over-the-air collaboration between local sensors are utilized to exploit the sample and feature diversity. Simulation results illustrate the importance of such collaborative training and the efficiency of the proposed transmission scheme.