RHFedMTL: Resource-Aware Hierarchical Federated Multi-Task Learning

The rapid development of artificial intelligence (AI) over massive applications including Internet-of-things on cellular network raises the concern of technical challenges such as privacy, heterogeneity and resource efficiency. Federated learning is an effective way to enable AI over massive distributed nodes with security. However, conventional works mostly focus on learning a single global model for a unique task across the network, and are generally less competent to handle multi-task learning (MTL) scenarios with stragglers at the expense of acceptable computation and communication cost. Meanwhile, it is challenging to ensure the privacy while maintain a coupled multi-task learning across multiple base stations (BSs) and terminals. In this paper, inspired by the natural cloud-BS-terminal hierarchy of cellular works, we provide a viable resource-aware hierarchical federated MTL (RHFedMTL) solution to meet the heterogeneity of tasks, by solving different tasks within the BSs and aggregating the multi-task result in the cloud without compromising the privacy. Specifically, a primal-dual method has been leveraged to effectively transform the coupled MTL into some local optimization sub-problems within BSs. Furthermore, compared with existing methods to reduce resource cost by simply changing the aggregation frequency, we dive into the intricate relationship between resource consumption and learning accuracy, and develop a resource-aware learning strategy for local terminals and BSs to meet the resource budget. Extensive simulation results demonstrate the effectiveness and superiority of RHFedMTL in terms of improving the learning accuracy and boosting the convergence rate.Comment: 11 pages, 8 figure

NetGPT: A Native-AI Network Architecture Beyond Provisioning Personalized Generative Services

Large language models (LLMs) have triggered tremendous success to empower daily life by generative information, and the personalization of LLMs could further contribute to their applications due to better alignment with human intents. Towards personalized generative services, a collaborative cloud-edge methodology sounds promising, as it facilitates the effective orchestration of heterogeneous distributed communication and computing resources. In this article, after discussing the pros and cons of several candidate cloud-edge collaboration techniques, we put forward NetGPT to capably deploy appropriate LLMs at the edge and the cloud in accordance with their computing capacity. In addition, edge LLMs could efficiently leverage location-based information for personalized prompt completion, thus benefiting the interaction with cloud LLMs. After deploying representative open-source LLMs (e.g., GPT-2-base and LLaMA model) at the edge and the cloud, we present the feasibility of NetGPT on the basis of low-rank adaptation-based light-weight fine-tuning. Subsequently, we highlight substantial essential changes required for a native artificial intelligence (AI) network architecture towards NetGPT, with special emphasis on deeper integration of communications and computing resources and careful calibration of logical AI workflow. Furthermore, we demonstrate several by-product benefits of NetGPT, given edge LLM's astonishing capability to predict trends and infer intents, which possibly leads to a unified solution for intelligent network management \& orchestration. In a nutshell, we argue that NetGPT is a promising native-AI network architecture beyond provisioning personalized generative services

Communication-Efficient Cooperative Multi-Agent PPO via Regulated Segment Mixture in Internet of Vehicles

Multi-Agent Reinforcement Learning (MARL) has become a classic paradigm to solve diverse, intelligent control tasks like autonomous driving in Internet of Vehicles (IoV). However, the widely assumed existence of a central node to implement centralized federated learning-assisted MARL might be impractical in highly dynamic scenarios, and the excessive communication overheads possibly overwhelm the IoV system. Therefore, in this paper, we design a communication efficient cooperative MARL algorithm, named RSM-MAPPO, to reduce the communication overheads in a fully distributed architecture. In particular, RSM-MAPPO enhances the multi-agent Proximal Policy Optimization (PPO) by incorporating the idea of segment mixture and augmenting multiple model replicas from received neighboring policy segments. Afterwards, RSM-MAPPO adopts a theory-guided metric to regulate the selection of contributive replicas to guarantee the policy improvement. Finally, extensive simulations in a mixed-autonomy traffic control scenario verify the effectiveness of the RSM-MAPPO algorithm

Genome sequence of the insect pathogenic fungus Cordyceps militaris, a valued traditional chinese medicine

Species in the ascomycete fungal genus Cordyceps have been proposed to be the teleomorphs of Metarhizium species. The latter have been widely used as insect biocontrol agents. Cordyceps species are highly prized for use in traditional Chinese medicines, but the genes responsible for biosynthesis of bioactive components, insect pathogenicity and the control of sexuality and fruiting have not been determined. Here, we report the genome sequence of the type species Cordyceps militaris. Phylogenomic analysis suggests that different species in the Cordyceps/Metarhizium genera have evolved into insect pathogens independently of each other, and that their similar large secretomes and gene family expansions are due to convergent evolution. However, relative to other fungi, including Metarhizium spp., many protein families are reduced in C. militaris, which suggests a more restricted ecology. Consistent with its long track record of safe usage as a medicine, the Cordyceps genome does not contain genes for known human mycotoxins. We establish that C. militaris is sexually heterothallic but, very unusually, fruiting can occur without an opposite mating-type partner. Transcriptional profiling indicates that fruiting involves induction of the Zn2Cys6-type transcription factors and MAPK pathway; unlike other fungi, however, the PKA pathway is not activated.https://doi.org/10.1186/gb-2011-12-11-r11

6G Network AI Architecture for Everyone-Centric Customized Services

Mobile communication standards were developed for enhancing transmission and network performance by using more radio resources and improving spectrum and energy efficiency. How to effectively address diverse user requirements and guarantee everyone's Quality of Experience (QoE) remains an open problem. The Sixth Generation (6G) mobile systems will solve this problem by utilizing heterogenous network resources and pervasive intelligence to support everyone-centric customized services anywhere and anytime. In this article, we first coin the concept of Service Requirement Zone (SRZ) on the user side to characterize and visualize the integrated service requirements and preferences of specific tasks of individual users. On the system side, we further introduce the concept of User Satisfaction Ratio (USR) to evaluate the system's overall service ability of satisfying a variety of tasks with different SRZs. Then, we propose a network Artificial Intelligence (AI) architecture with integrated network resources and pervasive AI capabilities for supporting customized services with guaranteed QoEs. Finally, extensive simulations show that the proposed network AI architecture can consistently offer a higher USR performance than the cloud AI and edge AI architectures with respect to different task scheduling algorithms, random service requirements, and dynamic network conditions

Temporal variability of visibility and its parameterizations in Ningbo, China

Simultaneous and continuous measurements of visibility, meteorological parameters and air pollutants were carried out at a suburban site in Ningbo from June 1, 2013 to May 31, 2015. The characteristics of visibility and their relationships with air pollutants and meteorological factors were investigated using multiple statistical methods. Daily visibility ranged from 0.6 to 34.1 km, with a mean value of 11.8 km. During the 2-year experiment, 43.4% of daily visibility was found to be less than 10.0 km and only 9.2% was greater than 20.0 km. Visibility was lower in winter with a frequency of 53.4% in the range of 0.0–5.0 km. Annual visibility had an obvious diurnal variation, with the lowest and highest visibility being 7.5 km at approximately 06:00 local time and 15.6 km at approximately 14:00 local time, respectively. Multiple correspondence analysis (MCA) indicated that the different ranges of visibility were significantly affected by different levels of pollutants and meteorological conditions. Based on the analyses, visibility was found to be an exponential function of PM2.5 concentrations within a certain range of relative humidity. Thus, non-linear models combining multiple linear regressions with exponential regression were subsequently developed using the data collected from June 2014 to May 2015, and the data from June 2013 to May 2014 was used to evaluate the performance of the model. It was demonstrated that the derived models can quantitatively describe the relationships between visibility, air quality and meteorological parameters in Ningbo

The Influence of Emotion Regulation on Estimation Strategy Execution in Individuals with Trait Anxiety

Previous studies have shown that some negative emotions hinder estimation strategy execution. However, these studies rarely investigate the influence of negative emotions on the estimation strategy execution in individuals with trait anxiety. The present study examines the relationship between negative emotions and trait anxiety in individuals’ estimation strategy execution. Moreover, it looks into the influence of different emotion regulation strategies on their estimation strategy execution. In October 2010, 803 college students were evaluated using the Trait Anxiety Scale. From these participants, individuals with high and low trait anxiety were selected to complete the double-digit multiplication estimation task. The results showed that the estimation strategy’s execution speed in individuals with high trait anxiety was slower than those with low trait anxiety under negative emotions (t (113) = −2.269, p = 0.025, d = 0.427). Both expression inhibition and cognitive reappraisal could significantly improve the execution speed of the estimation strategy in low trait anxiety (p < 0.001). For individuals with high trait anxiety, cognitive reappraisal regulating negative emotions can promote the estimation strategy’s execution speed (p = 0.031). However, the use of expression inhibition has no significant effect on estimation strategy execution (p = 0.101). In summary, the present study revealed that different emotion regulation strategies moderated the arithmetic strategy execution of individuals with trait anxiety, and cognitive reappraisal had a better effect in individuals with high trait anxiety

FedNC: A Secure and Efficient Federated Learning Method Inspired by Network Coding

Federated Learning (FL) is a promising distributed learning mechanism which still faces two major challenges, namely privacy breaches and system efficiency. In this work, we reconceptualize the FL system from the perspective of network information theory, and formulate an original FL communication framework, FedNC, which is inspired by Network Coding (NC). The main idea of FedNC is mixing the information of the local models by making random linear combinations of the original packets, before uploading for further aggregation. Due to the benefits of the coding scheme, both theoretical and experimental analysis indicate that FedNC improves the performance of traditional FL in several important ways, including security, throughput, and robustness. To the best of our knowledge, this is the first framework where NC is introduced in FL. As FL continues to evolve within practical network frameworks, more applications and variants can be further designed based on FedNC