In recent years, wireless federated learning (FL) has been proposed to support the mobile intelligent applications over the wireless network, which protects the data privacy and security by exchanging the parameter between mobile devices and the base station (BS). However, the learning latency increases with the neural network scale due to the limited local computing power and communication bandwidth. To tackle this issue, we introduce model pruning for wireless FL to reduce the neural network scale. Device selection is also considered to further improve the learning performance. By removing the stragglers with low computing power or bad channel condition, the model aggregation loss caused by model pruning can be alleviated and the communication overhead can be effectively reduced. We analyze the convergence rate and learning latency of the proposed model pruning method and formulate an optimization problem to maximize the convergence rate under the given learning latency budget via jointly optimizing the pruning ratio, device selection, and wireless resource allocation. By solving the problem, the closed-form solutions of pruning ratio and wireless resource allocation are derived and the threshold-based device selection strategy is developed. Finally, extensive experiments are carried out to demonstrate that the proposed model pruning algorithm outperforms other existing schemes.

In this letter, we apply the model compression, i.e., network pruning, into wireless federated learning (FL) system to mitigate the local computation and communication bottlenecks. Firstly, the convergence rate and learning latency of the FL system are mathematically analyzed. Then, an optimization problem is formulated to maximize the convergence rate while guaranteeing the learning latency via jointly optimizing the pruning ratio and spectrum allocation. Finally, the experimental results show that the proposed learning scheme can improve the performance of the wireless FL as compared with other conventional schemes.

Mobile edge computing (MEC) brings the potential to address the ever increasing computation demands from the mobile users (MUs). In addition to local processing, the resource-constrained MUs in an MEC system can also offload computation to the nearby servers for remote execution. With the explosive growth of mobile devices, computation offloading faces the challenge of spectrum congestion, which in turn deteriorates the overall quality of computation experience. This paper hence investigates computation task scheduling in a heterogeneous cellular and WiFi MEC system. Such a system provides both licensed and unlicensed spectrum opportunities. Due to the sharing of communication and computation resources as well as the uncertainties, we formulate the problem of computation task scheduling among the competing MUs in a stationary heterogeneous edge computing system as a non-cooperative stochastic game. We propose an approximation-based multi-agent Markov decision process without the global system state observations, under which a multi-agent proximal policy optimization (PPO) algorithm is derived to solve the corresponding Nash equilibrium. When expanding to a non-stationary heterogeneous edge computing system, the obtained algorithm suffers from the slow convergence due to constrained adaptability. Accordingly, we explore meta-learning and propose a multi-agent meta-PPO algorithm, which rapidly adapts the control policy learning to the non-stationarity. Numerical experiments demonstrate performance gains from our proposed algorithms.

In the realm of Sixth-Generation (6G) mobile communication systems, unmanned aerial vehicle (UAV) technologies have gained significant attention, particularly within UAV-assisted wireless networks. This paper explores radar-assisted predictive beamforming to enhance communication in such networks. A mobile UAV equipped with multiple antennas and radar functionality is deployed to serve a group of mobile users on the ground. The primary objective is to optimize communication performance, ensuring stable and efficient data transmission rates within a specified time frame, under practical power and flight range constraints. To address this challenging problem, we introduce a novel solution framework that integrates a deep reinforcement learning (DRL) network and a dual-layer deep-unfolding network (DUN). By leveraging a two-timescale processing structure, the new framework enables and facilitates the joint optimization of mobile user tracking, transmit beamforming and UAV trajectory. Simulation results confirm the effectiveness of our proposed scheme, demonstrating improvements in movement tracking accuracy and transmission rates.

In Federated Edge Learning (FEEL) networks, edge devices exchange the model parameters with each other to protect data privacy, instead of directly transmitting data samples. However, the learning performance may decrease due to the limited computation, communication, and storage resources. On the one hand, devices may not have sufficient storage for the redundant data samples. On the other hand, the model transmission and computation cause a large training latency. To address these issues, we develop a storage-aware user scheduling and bandwidth allocation Federated Learning (FL) algorithm with data cleansing by taking into consideration the storage resource, data influence, and channel state information. First, a data influence evaluation method is introduced by analyzing the model divergence in a communication round aroused by the data sample. Secondly, a probability-based user scheduling scheme is proposed by minimizing the weighted sum of the storage consumption, data influence, and uploading latency. Accordingly, the joint user scheduling and bandwidth allocation scheme is developed to minimize the maximum latency for local gradient uploading. Extensive experiments demonstrate that the proposed algorithm can significantly reduce the storage pressure and the training latency while improving the learning accuracy.

Infectious disease outbreaks transcend the medical and public health realms, triggering widespread panic and impeding socio-economic development. Considering that self-limiting diarrhoea of sporadic cases is usually underreported, the Salmonella outbreak (SO) study offers a unique opportunity for source tracing, spatiotemporal correlation, and outbreak prediction. To summarize the pattern of SO and estimate observational epidemiological indicators, 1,134 qualitative reports screened from 1949 to 2023 were included in the systematic review dataset, which contained a 506-study meta-analysis dataset. In addition to the dataset comprising over 50 columns with a total of 46,494 entries eligible for inclusion in systematic reviews or input into prediction models, we also provide initial literature collection datasets and datasets containing socio-economic and climate information for relevant regions. This study has a broad impact on advancing knowledge regarding epidemic trends and prevention priorities in diverse salmonellosis outbreaks and guiding rational policy-making or predictive modeling to mitigate the infringement upon the right to life imposed by significant epidemics.