Machine learning (ML) enhanced unmanned aerial vehicle (UAV) networks are envisioned to facilitate extensive applications in next-generation wireless networks. Due to the privacy concern and communication overhead in cloud-centric ML, federated reinforcement learning (FRL) enables UAVs to collaboratively train a policy model without disclosing raw observation data. However, the model aggregator in centralized FRL architecture poses various potential threats such as a single point of failure and is inappropriate to distributed networks with unreliable links and nodes. In this paper, we propose AirFRL, a topology-aware decentralized federated reinforcement learning framework for UAV-enabled networks. In AirFRL, we consider the topology dynamics influenced by nodes' mobility and communication quality and its impact on AirFRL. To accelerate training process and guarantee the model performance, we also incorporate the model compression to lighten the local model and introduce the consensus distance and data correlation to reflect the discrepancy between local models and local data. Furthermore, we propose an efficient algorithm to decide the optimal neighbour node selection and model compression ratio. A case study and numerical results demonstrate that AirFRL can achieve linear training speedup and guarantee the learning performance for UAV-enabled networks.

Drone networks are becoming increasingly significant in industrial Internet of Things (IIoT) applications. The limited resources of drones pose challenges in implementing robust security mechanisms that require substantial computation and power resources. Specifically, the inherent complexity of drone networks makes traditional intrusion detection systems (IDS) ineffective due to data imbalance and data scarcity. To address these challenges, this paper proposes a novel IDS framework that integrates conditional generative adversarial networks (CGANs) and utilizes the benefits from the systematic integration of foundation models within a federated learning (FL) paradigm. It leverages the CGANs to address the data issues ensures reliable performance and stable convergence against the foundation model. Moreover, our approach enhances data privacy relying on the differential privacy in FL and protects global model integrity through secure aggregation and updating. Simulation results show that the proposed framework achieve the accuracy rates of 91% and 99% on cyber and physical datasets, respectively. This framework achieves improvement ranging from 0.47% to 3.24% for cyber datasets and from 0.93% to 4.84% for physical datasets, which yields its superior performance in drone networks intrusion detection.

Federated learning (FL), utilizing data from the edge devices (EDs) while protecting user privacy has gained much attention. Its efficacy is substantially influenced by both the quantity of connected devices and the quality of wireless communications. Network congestion, resulting from multiple access and signal attenuation caused by physical obstacles may severely impact the convergence of the FL model. To address these issues, this article employs nonorthogonal multiple access (NOMA) for uplink transmission and designs a two-tier FL framework consisting of ground devices and unmanned aerial vehicles (UAVs) to ensure the construction of Line of Sight (LoS) channels from EDs to the base station. Moreover, we construct a multiobjective joint optimization problem to minimize the FL convergence time considering constraints, such as the NOMA uplink latency, ED selection strategy, local training latency, and energy consumption. We also deduce the theoretical upper bound of the convergence time and transform the proposed multiobjective problem into a solvable form by eliminating the discrete variables determined by the ED selection. In turn, we utilize the proximal policy optimization (PPO) algorithm to solve this optimization problem. Finally, the extensive experimental results demonstrate the advantages of our proposed algorithm in terms of latency and energy consumption, while yielding a high robustness and scalability.

Unmanned aerial vehicles (UAVs) empowered Internet of things (IoT) networks have emerged as a burgeoning paradigm in the era of 6G. However, due to substantial data volume and privacy concerns, the conventional UAV backhaul to cloud center framework is not applicable to various latency and privacy-sensitive applications. Therefore, we propose a blockchain-based hierarchical federated learning (FL) framework for UAV-enabled IoT networks. Specifically, we utilize the total data distance-aware device association to mitigate model impairment arising from imbalanced data distribution. Besides, we introduce a lightweight blockchain into FL to tackle the trust deficit caused in decentralized global model aggregation. Furthermore, we design an optimization framework that jointly orchestrating device association, wireless resource allocation, and UAV deployment, aiming at a balance between the learning latency and model accuracy. To address the formulated optimization problem, we proposed a two-stage algorithm that integrates both greedy strategy and soft actor-critic algorithm. Extensive experiments show that our proposed scheme outperforms contemporary relative to state-of-the-art alternatives.

The cloud-edge-end architecture is suitable for many essential scenarios, such as 5 G, the Internet of Things (IoT), and mobile edge computing. Under this architecture, cross-domain and cross-layer data sharing is commonly in need. Considering cross-domain data sharing under the zero-trust model, where each entity does not trust the others, existing solutions have certain problems regarding security, fairness, scalability, and efficiency. Aiming at solving these issues, we conduct the following research. First, a new plaintext checkable encryption scheme is constructed, which can be used on lightweight IoT devices to verify the ciphertext validity sent by a data owner. Second, we propose a new multi-domain cloud-edge-end architecture based on sharding blockchains and design a cross-domain data sharing scheme under the partial trust model to achieve security, scalability, and high performance. Third, a cross-domain data sharing scheme under the zero trust model is further designed, which can ensure the fairness of both parties in data sharing. Fourth, we give a formal security definition and analysis of cross-domain data sharing. Fifth, we conduct a detailed theoretical analysis of the protocol and give an in-depth functional test and performance test, including the throughput and latency of data sharing policy registration and execution.

Unmanned aerial vehicles (UAVs) associated with federated learning (FL) have been deemed as a prospective framework by utilizing private data generated in the edge devices. However, despite various privacy-preserving and cryptography technologies adopted at the data level, FL still faces a range of security threats to raw data considering the broadcast nature of wireless channel. In this paper, to facilitate the communication-efficiency and privacy-preservation capability, we propose a UAV-enhanced covert federated learning architecture over mmWave massive multiple input multiple output (MIMO) channel, where we harness the covert communication technique in FL in order to avoid eavesdropping of illegal wardens. To achieve a trade-off between the security performance and training cost, we formulate a joint optimization problem involving the UAV’s trajectory, transmitting power, analog beamforming, and the required accuracy of FL. Furthermore, we propose the multi-agent deep deterministic policy gradient (MADDPG) algorithm to solve the above-mentioned problem. Numerous simulations have been performed to demonstrate both the effectiveness and convergence of the proposed algorithm.