45 research outputs found
Split Learning in 6G Edge Networks
With the proliferation of distributed edge computing resources, the 6G mobile
network will evolve into a network for connected intelligence. Along this line,
the proposal to incorporate federated learning into the mobile edge has gained
considerable interest in recent years. However, the deployment of federated
learning faces substantial challenges as massive resource-limited IoT devices
can hardly support on-device model training. This leads to the emergence of
split learning (SL) which enables servers to handle the major training workload
while still enhancing data privacy. In this article, we offer a brief overview
of key advancements in SL and articulate its seamless integration with wireless
edge networks. We begin by illustrating the tailored 6G architecture to support
edge SL. Then, we examine the critical design issues for edge SL, including
innovative resource-efficient learning frameworks and resource management
strategies under a single edge server. Additionally, we expand the scope to
multi-edge scenarios, exploring multi-edge collaboration and mobility
management from a networking perspective. Finally, we discuss open problems for
edge SL, including convergence analysis, asynchronous SL and U-shaped SL.Comment: 7 pages, 6 figure
Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities
Large language models (LLMs), which have shown remarkable capabilities, are
revolutionizing AI development and potentially shaping our future. However,
given their multimodality, the status quo cloud-based deployment faces some
critical challenges: 1) long response time; 2) high bandwidth costs; and 3) the
violation of data privacy. 6G mobile edge computing (MEC) systems may resolve
these pressing issues. In this article, we explore the potential of deploying
LLMs at the 6G edge. We start by introducing killer applications powered by
multimodal LLMs, including robotics and healthcare, to highlight the need for
deploying LLMs in the vicinity of end users. Then, we identify the critical
challenges for LLM deployment at the edge and envision the 6G MEC architecture
for LLMs. Furthermore, we delve into two design aspects, i.e., edge training
and edge inference for LLMs. In both aspects, considering the inherent resource
limitations at the edge, we discuss various cutting-edge techniques, including
split learning/inference, parameter-efficient fine-tuning, quantization, and
parameter-sharing inference, to facilitate the efficient deployment of LLMs.
This article serves as a position paper for thoroughly identifying the
motivation, challenges, and pathway for empowering LLMs at the 6G edge.Comment: 7 pages, 5 figure
Towards Full-scene Domain Generalization in Multi-agent Collaborative Bird's Eye View Segmentation for Connected and Autonomous Driving
Collaborative perception has recently gained significant attention in
autonomous driving, improving perception quality by enabling the exchange of
additional information among vehicles. However, deploying collaborative
perception systems can lead to domain shifts due to diverse environmental
conditions and data heterogeneity among connected and autonomous vehicles
(CAVs). To address these challenges, we propose a unified domain generalization
framework applicable in both training and inference stages of collaborative
perception. In the training phase, we introduce an Amplitude Augmentation
(AmpAug) method to augment low-frequency image variations, broadening the
model's ability to learn across various domains. We also employ a
meta-consistency training scheme to simulate domain shifts, optimizing the
model with a carefully designed consistency loss to encourage domain-invariant
representations. In the inference phase, we introduce an intra-system domain
alignment mechanism to reduce or potentially eliminate the domain discrepancy
among CAVs prior to inference. Comprehensive experiments substantiate the
effectiveness of our method in comparison with the existing state-of-the-art
works. Code will be released at https://github.com/DG-CAVs/DG-CoPerception.git
Optimal Resource Allocation for U-Shaped Parallel Split Learning
Split learning (SL) has emerged as a promising approach for model training
without revealing the raw data samples from the data owners. However,
traditional SL inevitably leaks label privacy as the tail model (with the last
layers) should be placed on the server. To overcome this limitation, one
promising solution is to utilize U-shaped architecture to leave both early
layers and last layers on the user side. In this paper, we develop a novel
parallel U-shaped split learning and devise the optimal resource optimization
scheme to improve the performance of edge networks. In the proposed framework,
multiple users communicate with an edge server for SL. We analyze the
end-to-end delay of each client during the training process and design an
efficient resource allocation algorithm, called LSCRA, which finds the optimal
computing resource allocation and split layers. Our experimental results show
the effectiveness of LSCRA and that U-shaped PSL can achieve a similar
performance with other SL baselines while preserving label privacy. Index
Terms: U-shaped network, split learning, label privacy, resource allocation,
5G/6G edge networks.Comment: 6 pages, 6 figure
Sequence Dependent Repair of 1,N6-Ethenoadenine by DNA Repair Enzymes ALKBH2, ALKBH3, and AlkB
Mutation patterns of DNA adducts, such as mutational spectra and signatures, are useful tools for diagnostic and prognostic purposes. Mutational spectra of carcinogens derive from three sources: adduct formation, replication bypass, and repair. Here, we consider the repair aspect of 1,N6-ethenoadenine (εA) by the 2-oxoglutarate/Fe(II)-dependent AlkB family enzymes. Specifically, we investigated εA repair across 16 possible sequence contexts (5′/3′ flanking base to εA varied as G/A/T/C). The results revealed that repair efficiency is altered according to sequence, enzyme, and strand context (ss- versus ds-DNA). The methods can be used to study other aspects of mutational spectra or other pathways of repair
Vehicle as a Service (VaaS): Leverage Vehicles to Build Service Networks and Capabilities for Smart Cities
Smart cities demand resources for rich immersive sensing, ubiquitous
communications, powerful computing, large storage, and high intelligence
(SCCSI) to support various kinds of applications, such as public safety,
connected and autonomous driving, smart and connected health, and smart living.
At the same time, it is widely recognized that vehicles such as autonomous
cars, equipped with significantly powerful SCCSI capabilities, will become
ubiquitous in future smart cities. By observing the convergence of these two
trends, this article advocates the use of vehicles to build a cost-effective
service network, called the Vehicle as a Service (VaaS) paradigm, where
vehicles empowered with SCCSI capability form a web of mobile servers and
communicators to provide SCCSI services in smart cities. Towards this
direction, we first examine the potential use cases in smart cities and
possible upgrades required for the transition from traditional vehicular ad hoc
networks (VANETs) to VaaS. Then, we will introduce the system architecture of
the VaaS paradigm and discuss how it can provide SCCSI services in future smart
cities, respectively. At last, we identify the open problems of this paradigm
and future research directions, including architectural design, service
provisioning, incentive design, and security & privacy. We expect that this
paper paves the way towards developing a cost-effective and sustainable
approach for building smart cities.Comment: 32 pages, 11 figure
Adaptive Communications in Collaborative Perception with Domain Alignment for Autonomous Driving
Collaborative perception among multiple connected and autonomous vehicles can
greatly enhance perceptive capabilities by allowing vehicles to exchange
supplementary information via communications. Despite advances in previous
approaches, challenges still remain due to channel variations and data
heterogeneity among collaborative vehicles. To address these issues, we propose
ACC-DA, a channel-aware collaborative perception framework to dynamically
adjust the communication graph and minimize the average transmission delay
while mitigating the side effects from the data heterogeneity. Our novelties
lie in three aspects. We first design a transmission delay minimization method,
which can construct the communication graph and minimize the transmission delay
according to different channel information state. We then propose an adaptive
data reconstruction mechanism, which can dynamically adjust the rate-distortion
trade-off to enhance perception efficiency. Moreover, it minimizes the temporal
redundancy during data transmissions. Finally, we conceive a domain alignment
scheme to align the data distribution from different vehicles, which can
mitigate the domain gap between different vehicles and improve the performance
of the target task. Comprehensive experiments demonstrate the effectiveness of
our method in comparison to the existing state-of-the-art works.Comment: 6 pages, 6 figure
Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks
The increasingly deeper neural networks hinder the democratization of
privacy-enhancing distributed learning, such as federated learning (FL), to
resource-constrained devices. To overcome this challenge, in this paper, we
advocate the integration of edge computing paradigm and parallel split learning
(PSL), allowing multiple client devices to offload substantial training
workloads to an edge server via layer-wise model split. By observing that
existing PSL schemes incur excessive training latency and large volume of data
transmissions, we propose an innovative PSL framework, namely, efficient
parallel split learning (EPSL), to accelerate model training. To be specific,
EPSL parallelizes client-side model training and reduces the dimension of local
gradients for back propagation (BP) via last-layer gradient aggregation,
leading to a significant reduction in server-side training and communication
latency. Moreover, by considering the heterogeneous channel conditions and
computing capabilities at client devices, we jointly optimize subchannel
allocation, power control, and cut layer selection to minimize the per-round
latency. Simulation results show that the proposed EPSL framework significantly
decreases the training latency needed to achieve a target accuracy compared
with the state-of-the-art benchmarks, and the tailored resource management and
layer split strategy can considerably reduce latency than the counterpart
without optimization.Comment: 15 pages, 13 figure