UAV swarms are widely used in emergency communications, area monitoring, and disaster relief. Coordinated by control centers, they are ideal for federated learning (FL) frameworks. However, current UAV-assisted FL methods primarily focus on single tasks, overlooking the need for multi-task training. In disaster relief scenarios, UAVs perform tasks such as crowd detection, road feasibility analysis, and disaster assessment, which exhibit time-varying demands and potential correlations. In order to meet the time-varying requirements of tasks and complete multiple tasks efficiently under resource constraints, in this paper, we propose a UAV swarm based multi-task FL framework, where ground emergency vehicles (EVs) collaborate with UAVs to accomplish multiple tasks efficiently under constrained energy and bandwidth resources. Through theoretical analysis, we identify key factors affecting task performance and introduce a task attention mechanism to dynamically evaluate task importance, thereby achieving efficient resource allocation. Additionally, we propose a task affinity (TA) metric to capture the dynamic correlation among tasks, thereby promoting task knowledge sharing to accelerate training and improve the generalization ability of the model in different scenarios. To optimize resource allocation, we formulate a two-layer optimization problem to jointly optimize UAV transmission power, computation frequency, bandwidth allocation, and UAV-EV associations. For the inner problem, we derive closed-form solutions for transmission power, computation frequency, and bandwidth allocation and apply a block coordinate descent method for optimization. For the outer problem, a two-stage algorithm is designed to determine optimal UAV-EV associations. Furthermore, theoretical analysis reveals a trade-off between UAV energy consumption and multi-task performance.

--Federated learning enables distributed model training across clients under central coordination without raw data exchange. However, in wireless implementations, frequent parameter updates between the server and clients create significant communication overhead. While existing research assumes either known channel state information (CSI) or that the channel follows a stationary distribution, practical wireless channels exhibit non-stationary characteristics due to channel fading, user mobility, and hostile attacks in telecommunication networks. The unavailability of both CSI and time-varying channel distribution can lead to unpredictable failures in parameter transmission, exacerbating clients staleness thus affecting model convergence. T o address these challenges, we propose an asynchronous federated learning scheduling framework for non-stationary channel environments, designed to reduce clients staleness while promoting both fair and efficient communication and aggregation. This framework considers two channel scenarios: extremely non-stationary and piecewise-stationary channels. Age of Information (AoI) serves as a metric to quantify client staleness under non-stationary conditions. Firstly, we perform a rigorous convergence analysis to explore the impact of AoI and per-round client participation on learning performance. The channel scheduling problem in the non-stationary scenario is addressed and formulated within the multi-armed bandit (MAB) framework and we derive the achievable theoretical lower bounds on the AoI regret. Based on this framework, we propose corresponding scheduling strategies for the two non-stationary channel scenarios that leverage the foundations of the GLR-CUCB and M-exp3 algorithms, along with derivations of their respective upper bounds on AoI regret. Additionally, to address the issue of imbalanced client updates in non-stationary channels, we introduce an adaptive matching strategy that incorporates considerations of marginal utility and fairness of clients. Simulation results demonstrate that the proposed algorithm achieves sub-linear growth in AoI regret, accelerates federated learning convergence, and promotes fairer aggregation. HE proliferation of Internet of Things (IoT) devices and the rise of edge computing have resulted in an increasingly decentralized distribution of data across end devices, such as smartphones and sensors. In traditional centralized machine learning approaches, data consolidation at a single location is required, which raises privacy concerns and incurs significant communication overhead.

The rapid development of Unmanned aerial vehicles (UAVs) technology has spawned a wide variety of applications, such as emergency communications, regional surveillance, and disaster relief. Due to their limited battery capacity and processing power, multiple UAVs are often required for complex tasks. In such cases, a control center is crucial for coordinating their activities, which fits well with the federated learning (FL) framework. However, conventional FL approaches often focus on a single task, ignoring the potential of training multiple related tasks simultaneously. In this paper, we propose a UAV-assisted multi-task federated learning scheme, in which data collected by multiple UAVs can be used to train multiple related tasks concurrently. The scheme facilitates the training process by sharing feature extractors across related tasks and introduces a task attention mechanism to balance task performance and encourage knowledge sharing. To provide an analytical description of training performance, the convergence analysis of the proposed scheme is performed. Additionally, the optimal bandwidth allocation for UAVs under limited bandwidth conditions is derived to minimize communication time. Meanwhile, a UAV-EV association strategy based on coalition formation game is proposed. Simulation results validate the effectiveness of the proposed scheme in enhancing multi-task performance and training speed.

The two-dimensional electron gas (2DEG) is a fundamental model, which is drawing increasing interest because of recent advances in experimental and theoretical studies of 2D materials. Current understanding of the ground state of the 2DEG relies on quantum Monte Carlo calculations, based on variational comparisons of different ansatze for different phases. We use a single variational ansatz, a general backflow-type wave function using a message-passing neural quantum state architecture, for a unified description across the entire density range. The variational optimization consistently leads to lower ground-state energies than previous best results. Transition into a Wigner crystal (WC) phase occurs automatically at rs = 37 +/- 1, a density lower than currently believed. Between the liquid and WC phases, the same ansatz and variational search strongly suggest the existence of intermediate states in a broad range of densities, with enhanced short-range nematic spin correlations.