13 research outputs found
Everybody Compose: Deep Beats To Music
This project presents a deep learning approach to generate monophonic
melodies based on input beats, allowing even amateurs to create their own music
compositions. Three effective methods - LSTM with Full Attention, LSTM with
Local Attention, and Transformer with Relative Position Representation - are
proposed for this novel task, providing great variation, harmony, and structure
in the generated music. This project allows anyone to compose their own music
by tapping their keyboards or ``recoloring'' beat sequences from existing
works.Comment: Accepted MMSys '2
User Dynamics-Aware Edge Caching and Computing for Mobile Virtual Reality
In this paper, we present a novel content caching and delivery approach for
mobile virtual reality (VR) video streaming. The proposed approach aims to
maximize VR video streaming performance, i.e., minimizing video frame missing
rate, by proactively caching popular VR video chunks and adaptively scheduling
computing resources at an edge server based on user and network dynamics.
First, we design a scalable content placement scheme for deciding which video
chunks to cache at the edge server based on tradeoffs between computing and
caching resource consumption. Second, we propose a machine learning-assisted VR
video delivery scheme, which allocates computing resources at the edge server
to satisfy video delivery requests from multiple VR headsets. A Whittle
index-based method is adopted to reduce the video frame missing rate by
identifying network and user dynamics with low signaling overhead. Simulation
results demonstrate that the proposed approach can significantly improve VR
video streaming performance over conventional caching and computing resource
scheduling strategies.Comment: 38 pages, 13 figures, single column double spaced, published in IEEE
Journal of Selected Topics in Signal Processin
Slicing-Based Artificial Intelligence Service Provisioning on the Network Edge: Balancing AI Service Performance and Resource Consumption of Data Management
Edge intelligence leverages computing resources on the network edge to provide artificial intelligence (AI) services close to network users. As it enables fast inference and distributed learning, edge intelligence is envisioned to be an important component of 6G networks. In this article, we investigate AI service provisioning for supporting edge intelligence. First, we present the features and requirements of AI services. Then we introduce AI service data management and customize network slicing for AI services. Specifically, we propose a novel resource-pooling method to regularize service data exchange within the network edge while allocating network resources for AI services. Using this method, network resources can be properly allocated to network slices to fulfill AI service requirements. A trace-driven case study demonstrates that the proposed method can allow network slicing to satisfy diverse AI service performance requirements via the flexible selection of resource-pooling policies. In this study, we illustrate the necessity, challenge, and potential of AI service provisioning on the network edge and provide insights into resource management for AI services
Digital Twin-Driven Computing Resource Management for Vehicular Networks
This paper presents a novel approach for computing resource management of
edge servers in vehicular networks based on digital twins and artificial
intelligence (AI). Specifically, we construct two-tier digital twins tailored
for vehicular networks to capture networking-related features of vehicles and
edge servers. By exploiting such features, we propose a two-stage computing
resource allocation scheme. First, the central controller periodically
generates reference policies for real-time computing resource allocation
according to the network dynamics and service demands captured by digital twins
of edge servers. Second, computing resources of the edge servers are allocated
in real time to individual vehicles via low-complexity matching-based
allocation that complies with the reference policies. By leveraging digital
twins, the proposed scheme can adapt to dynamic service demands and vehicle
mobility in a scalable manner. Simulation results demonstrate that the proposed
digital twin-driven scheme enables the vehicular network to support more
computing tasks than benchmark schemes.Comment: 6 pages, 4 figures, accepted by 2022 IEEE GLOBECO
Digital Twin Based User-Centric Resource Management for Multicast Short Video Streaming
Multicast short video streaming (MSVS) can effectively reduce network traffic
load by delivering identical video sequences to multiple users simultaneously.
The existing MSVS schemes mainly rely on the aggregated video requests to
reserve bandwidth and computing resources, which cannot satisfy users' diverse
and dynamic service requirements, particularly when users' swipe behaviors
exhibit spatiotemporal fluctuation. In this paper, we propose a user-centric
resource management scheme based on the digital twin (DT) technique, which aims
to enhance user satisfaction as well as reduce resource consumption. Firstly,
we design a user DT (UDT)-assisted resource reservation framework.
Specifically, UDTs are constructed for individual users, which store users'
historical data for updating multicast groups and abstracting useful
information. The swipe probability distributions and recommended video lists
are abstracted from UDTs to predict bandwidth and computing resource demands.
Parameterized sigmoid functions are leveraged to characterize multicast groups'
user satisfaction. Secondly, we formulate a joint non-convex bandwidth and
computing resource reservation problem which is transformed into a convex
piecewise problem by utilizing a tangent function to approximately substitute
the concave part. A low-complexity scheduling algorithm is then developed to
find the optimal resource reservation decisions. Simulation results based on
the real-world dataset demonstrate that the proposed scheme outperforms
benchmark schemes in terms of user satisfaction and resource consumption.Comment: 13 pages, 11 figure
ELSA: Secure Aggregation for Federated Learning with Malicious Actors
Federated learning (FL) is an increasingly popular
approach for machine learning (ML) in cases where the train-
ing dataset is highly distributed. Clients perform local training
on their datasets and the updates are then aggregated into
the global model. Existing protocols for aggregation are either
inefficient, or don’t consider the case of malicious actors in the
system. This is a major barrier in making FL an ideal solution
for privacy-sensitive ML applications. We present ELSA, a
secure aggregation protocol for FL, which breaks this barrier -
it is efficient and addresses the existence of malicious actors at
the core of its design. Similar to prior work on Prio and Prio+,
ELSA provides a novel secure aggregation protocol built out of
distributed trust across two servers that keeps individual client
updates private as long as one server is honest, defends against
malicious clients and is efficient end-to-end. Compared to prior
works, the distinguishing theme in ELSA is that instead of the
servers generating cryptographic correlations interactively, the
clients act as untrusted dealers of these correlations without
compromising the protocol’s security. This leads to a much
faster protocol while also achieving stronger security at that ef-
ficiency compared to prior work. We introduce new techniques
that retain privacy even when a server is malicious at a small
added cost of 7-25% in runtime with negligible increase in
communication over the case of semi-honest server. Our work
improves end-to-end runtime over prior work with similar
security guarantees by big margins - single-aggregator RoFL
by up to 305x (for the models we consider), and distributed
trust Prio by up to 8
Split Learning over Wireless Networks: Parallel Design and Resource Management
Split learning (SL) is a collaborative learning framework, which can train an
artificial intelligence (AI) model between a device and an edge server by
splitting the AI model into a device-side model and a server-side model at a
cut layer. The existing SL approach conducts the training process sequentially
across devices, which incurs significant training latency especially when the
number of devices is large. In this paper, we design a novel SL scheme to
reduce the training latency, named Cluster-based Parallel SL (CPSL) which
conducts model training in a "first-parallel-then-sequential" manner.
Specifically, the CPSL is to partition devices into several clusters,
parallelly train device-side models in each cluster and aggregate them, and
then sequentially train the whole AI model across clusters, thereby
parallelizing the training process and reducing training latency. Furthermore,
we propose a resource management algorithm to minimize the training latency of
CPSL considering device heterogeneity and network dynamics in wireless
networks. This is achieved by stochastically optimizing the cut layer
selection, real-time device clustering, and radio spectrum allocation. The
proposed two-timescale algorithm can jointly make the cut layer selection
decision in a large timescale and device clustering and radio spectrum
allocation decisions in a small timescale. Extensive simulation results on
non-independent and identically distributed data demonstrate that the proposed
solutions can greatly reduce the training latency as compared with the existing
SL benchmarks, while adapting to network dynamics.Comment: The paper has been submitted to IEEE Journal on Selected Areas in
Communication