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The aim of this paper is to explore bioinspired vertiport designs—a hub for drones’ vertical takeoff and landing (VTOL) and servicing, also referred to as a nesting station, docking station, hangar, or landing station—for drone swarms tasked with specific missions. The vertiport system design is inspired by tree structures, with branches represented by capsules that house drones. Solar panels mounted on actuators at the top of the vertiport adjust their orientation to maximize sun exposure, supplying power to the vertiport’s isolated grid for continuous energy day and night. A weather station located at the top transmits data to a computing system, ensuring environmental safety for drone operations. The vertiport’s key components include capsules that open and close during drone launch and landing. Each capsule is equipped with charging contacts for the drones, AprilTags to facilitate precise landing, and a mechanism to center the drone within the capsule upon closure. Designed to protect the drones from environmental conditions, these capsules feature robust structures capable of withstanding harsh weather, ensuring the drones are safeguarded inside. This design highlights the potential of bioinspired approaches in creating efficient vertiport systems. 

Federated Learning (FL) revolutionizes collaborative machine learning among Internet of Things (IoT) devices by enabling them to train models collectively while preserving data privacy. FL algorithms fall into two primary categories: synchronous and asynchronous. While synchronous FL efficiently handles straggler devices, its convergence speed and model accuracy can be compromised. In contrast, asynchronous FL allows all devices to participate but incurs high communication overhead and potential model staleness. To overcome these limitations, the paper introduces a semi-synchronous FL framework that uses client tiering based on computing and communication latencies. Clients in different tiers upload their local models at distinct frequencies, striking a balance between straggler mitigation and communication costs. Building on this, the paper proposes the Dynamic client clustering, bandwidth allocation, and local training for semi-synchronous Federated learning (DecantFed) algorithm to dynamically optimize client clustering, bandwidth allocation, and local training workloads in order to maximize data sample processing rates in FL. DecantFed dynamically optimizes client clustering, bandwidth allocation, and local training workloads for maximizing data processing rates in FL. It also adapts client learning rates according to their tiers, thus addressing the model staleness issue. Extensive simulations using benchmark datasets like MNIST and CIFAR-10, under both IID and non-IID scenarios, demonstrate DecantFed’s superior performance. It outperforms FedAvg and FedProx in convergence speed and delivers at least a 28% improvement in model accuracy, compared to FedProx.