242 research outputs found
Towards an optimal bus frequency scheduling: When the waiting time matters
National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ
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
Experimental investigations of quasi-coherent micro-instabilities in Ohmic plasmas
The ITG and TEM instabilities with quasi-coherent spectra have been
identified experimentally, by the newly developed far-forward collective
scattering measurements in J-TEXT tokamak Ohmical plasmas. The ITG mode has
characteristic frequencies in the range of 30-100kHz and wavenumber of
k_\theta\rho_s<0.3. After the plasma density exceeds at critical value, the ITG
mode shows a bifurcation behavior, featured by frequency decrease and amplitude
enhancement. Meanwhile, the ion energy loss enhancement and confinement
degradation are also observed. It gives the direct experimental evidence for
ion thermal transport caused by ITG instability
Reinforcement of natural rubber with core-shell structure silica-poly(Methyl Methacrylate) nanoparticles
A highly performing natural rubber/silica (NR/SiO2) nanocomposite with a SiO2 loading of 2 wt% was prepared by combining similar dissolve mutually theory with latex compounding techniques. Before polymerization, double bonds were introduced onto the surface of the SiO2 particles with the silane-coupling agent. The core-shell structure silica-poly(methyl methacrylate), SiO2-PMMA, nanoparticles were formed by grafting polymerization of MMA on the surface of the modified SiO2 particles via in situ emulsion, and then NR/SiO2 nanocomposite was prepared by blending SiO2-PMMA and PMMA-modified NR (NR-PMMA). The Fourier transform infrared spectroscopy results show that PMMA has been successfully introduced onto the surface of SiO2, which can be well dispersed in NR matrix and present good interfacial adhesion with NR phase. Compared with those of pure NR, the thermal resistance and tensile properties of NR/SiO2 nanocomposite are significantly improved
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
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