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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. 

Modern advances in unmanned aerial vehicle (UAV) technology have widened the scope of commercial and military applications. However, the increased dependency on wireless communications exposes UAVs to potential attacks and introduces new threats, especially from UAVs designed with the malicious intent of targeting vital infrastructures. Significant efforts have been made from researchers and other United States (U.S.) Department of Defense (DoD) agencies for developing countermeasures for detection, interception, or destruction of the malicious UAVs. One promising countermeasure is the use of a counter UAV (CUAV) swarm to detect, track, and neutralize the malicious UAV. This paper aims to recognize the state-of-the-art swarm intelligence algorithms for achieving cooperative capture of a mobile target UAV. The major design and implementation challenges for swarm control, algorithm architecture, and safety protocols are considered. A prime challenge for UAV swarms is a robust communication infrastructure to enable accurate data transfer between UAVs for efficient path planning. A multi-agent deep reinforcement learning approach is applied to train a group of CUAVs to intercept a faster malicious UAV, while avoiding collisions among other CUAVs and non-cooperating obstacles (i.e. other aerial objects maneuvering in the area). The impact of the latency incurred through UAV-to-UAV communications is showcased and discussed with preliminary numerical results.