Multi-Stage Hybrid Federated Learning over Large-Scale Wireless Fog Networks
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One of the popular methods for distributed machine learning (ML) is federated learning, in which devices train local models based on their datasets, which are in turn aggregated periodically by a server. In large-scale fog networks, the "star" learning topology of federated learning poses several challenges in terms of resource utilization. We develop multi-stage hybrid model training (MH-MT), a novel learning methodology for distributed ML in these scenarios. Leveraging the hierarchical structure of fog systems, MH-MT combines multi-stage parameter relaying with distributed consensus formation among devices in a hybrid learning paradigm across network layers. We theoretically derive the convergence bound of MH-MT with respect to the network topology, ML model, and algorithm parameters such as the rounds of consensus employed in different clusters of devices. We obtain a set of policies for the number of consensus rounds at different clusters to guarantee either a finite optimality gap or convergence to the global optimum. Subsequently, we develop an adaptive distributed control algorithm for MH-MT to tune the number of consensus rounds at each cluster of local devices over time to meet convergence criteria. Our numerical experiments validate the performance of MH-MT in terms of convergence speed and resource utilization.
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