1,870 research outputs found
User Assignment and Resource Allocation for Hierarchical Federated Learning over Wireless Networks
The large population of wireless users is a key driver of data-crowdsourced
Machine Learning (ML). However, data privacy remains a significant concern.
Federated Learning (FL) encourages data sharing in ML without requiring data to
leave users' devices but imposes heavy computation and communications overheads
on mobile devices. Hierarchical FL (HFL) alleviates this problem by performing
partial model aggregation at edge servers. HFL can effectively reduce energy
consumption and latency through effective resource allocation and appropriate
user assignment. Nevertheless, resource allocation in HFL involves optimizing
multiple variables, and the objective function should consider both energy
consumption and latency, making the development of resource allocation
algorithms very complicated. Moreover, it is challenging to perform user
assignment, which is a combinatorial optimization problem in a large search
space. This article proposes a spectrum resource optimization algorithm (SROA)
and a two-stage iterative algorithm (TSIA) for HFL. Given an arbitrary user
assignment pattern, SROA optimizes CPU frequency, transmit power, and bandwidth
to minimize system cost. TSIA aims to find a user assignment pattern that
considerably reduces the total system cost. Experimental results demonstrate
the superiority of the proposed HFL framework over existing studies in energy
and latency reduction.Comment: Under review by IEEE Transactions on Communication
Facial Landmark Predictions with Applications to Metaverse
This research aims to make metaverse characters more realistic by adding lip
animations learnt from videos in the wild. To achieve this, our approach is to
extend Tacotron 2 text-to-speech synthesizer to generate lip movements together
with mel spectrogram in one pass. The encoder and gate layer weights are
pre-trained on LJ Speech 1.1 data set while the decoder is retrained on 93
clips of TED talk videos extracted from LRS 3 data set. Our novel decoder
predicts displacement in 20 lip landmark positions across time, using labels
automatically extracted by OpenFace 2.0 landmark predictor. Training converged
in 7 hours using less than 5 minutes of video. We conducted ablation study for
Pre/Post-Net and pre-trained encoder weights to demonstrate the effectiveness
of transfer learning between audio and visual speech data
Efficient Dropout-resilient Aggregation for Privacy-preserving Machine Learning
With the increasing adoption of data-hungry machine learning algorithms,
personal data privacy has emerged as one of the key concerns that could hinder
the success of digital transformation. As such, Privacy-Preserving Machine
Learning (PPML) has received much attention from both academia and industry.
However, organizations are faced with the dilemma that, on the one hand, they
are encouraged to share data to enhance ML performance, but on the other hand,
they could potentially be breaching the relevant data privacy regulations.
Practical PPML typically allows multiple participants to individually train
their ML models, which are then aggregated to construct a global model in a
privacy-preserving manner, e.g., based on multi-party computation or
homomorphic encryption. Nevertheless, in most important applications of
large-scale PPML, e.g., by aggregating clients' gradients to update a global
model for federated learning, such as consumer behavior modeling of mobile
application services, some participants are inevitably resource-constrained
mobile devices, which may drop out of the PPML system due to their mobility
nature. Therefore, the resilience of privacy-preserving aggregation has become
an important problem to be tackled. In this paper, we propose a scalable
privacy-preserving aggregation scheme that can tolerate dropout by participants
at any time, and is secure against both semi-honest and active malicious
adversaries by setting proper system parameters. By replacing
communication-intensive building blocks with a seed homomorphic pseudo-random
generator, and relying on the additive homomorphic property of Shamir secret
sharing scheme, our scheme outperforms state-of-the-art schemes by up to
6.37 in runtime and provides a stronger dropout-resilience. The
simplicity of our scheme makes it attractive both for implementation and for
further improvements.Comment: 16 pages, 5 figures. Accepted by IEEE Transactions on Information
Forensics and Securit
A Hybrid Framework of Reinforcement Learning and Convex Optimization for UAV-Based Autonomous Metaverse Data Collection
Unmanned aerial vehicles (UAVs) are promising for providing communication
services due to their advantages in cost and mobility, especially in the
context of the emerging Metaverse and Internet of Things (IoT). This paper
considers a UAV-assisted Metaverse network, in which UAVs extend the coverage
of the base station (BS) to collect the Metaverse data generated at roadside
units (RSUs). Specifically, to improve the data collection efficiency, resource
allocation and trajectory control are integrated into the system model. The
time-dependent nature of the optimization problem makes it non-trivial to be
solved by traditional convex optimization methods. Based on the proposed
UAV-assisted Metaverse network system model, we design a hybrid framework with
reinforcement learning and convex optimization to {cooperatively} solve the
time-sequential optimization problem. Simulation results show that the proposed
framework is able to reduce the mission completion time with a given
transmission power resource.Comment: This paper appears in IEEE Network magazin
Knowledge Is Flat: A Seq2Seq Generative Framework for Various Knowledge Graph Completion
Knowledge Graph Completion (KGC) has been recently extended to multiple
knowledge graph (KG) structures, initiating new research directions, e.g.
static KGC, temporal KGC and few-shot KGC. Previous works often design KGC
models closely coupled with specific graph structures, which inevitably results
in two drawbacks: 1) structure-specific KGC models are mutually incompatible;
2) existing KGC methods are not adaptable to emerging KGs. In this paper, we
propose KG-S2S, a Seq2Seq generative framework that could tackle different
verbalizable graph structures by unifying the representation of KG facts into
"flat" text, regardless of their original form. To remedy the KG structure
information loss from the "flat" text, we further improve the input
representations of entities and relations, and the inference algorithm in
KG-S2S. Experiments on five benchmarks show that KG-S2S outperforms many
competitive baselines, setting new state-of-the-art performance. Finally, we
analyze KG-S2S's ability on the different relations and the Non-entity
Generations.Comment: COLING 2022 Main Conferenc
FedPEAT: Convergence of Federated Learning, Parameter-Efficient Fine Tuning, and Emulator Assisted Tuning for Artificial Intelligence Foundation Models with Mobile Edge Computing
The emergence of foundation models, including language and vision models, has
reshaped AI's landscape, offering capabilities across various applications.
Deploying and fine-tuning these large models, like GPT-3 and BERT, presents
challenges, especially in the current foundation model era. We introduce
Emulator-Assisted Tuning (EAT) combined with Parameter-Efficient Fine-Tuning
(PEFT) to form Parameter-Efficient Emulator-Assisted Tuning (PEAT). Further, we
expand this into federated learning as Federated PEAT (FedPEAT). FedPEAT uses
adapters, emulators, and PEFT for federated model tuning, enhancing model
privacy and memory efficiency. Adapters adjust pre-trained models, while
emulators give a compact representation of original models, addressing both
privacy and efficiency. Adaptable to various neural networks, our approach also
uses deep reinforcement learning for hyper-parameter optimization. We tested
FedPEAT in a unique scenario with a server participating in collaborative
federated tuning, showcasing its potential in tackling foundation model
challenges
Traceable and Authenticable Image Tagging for Fake News Detection
To prevent fake news images from misleading the public, it is desirable not
only to verify the authenticity of news images but also to trace the source of
fake news, so as to provide a complete forensic chain for reliable fake news
detection. To simultaneously achieve the goals of authenticity verification and
source tracing, we propose a traceable and authenticable image tagging approach
that is based on a design of Decoupled Invertible Neural Network (DINN). The
designed DINN can simultaneously embed the dual-tags, \textit{i.e.},
authenticable tag and traceable tag, into each news image before publishing,
and then separately extract them for authenticity verification and source
tracing. Moreover, to improve the accuracy of dual-tags extraction, we design a
parallel Feature Aware Projection Model (FAPM) to help the DINN preserve
essential tag information. In addition, we define a Distance Metric-Guided
Module (DMGM) that learns asymmetric one-class representations to enable the
dual-tags to achieve different robustness performances under malicious
manipulations. Extensive experiments, on diverse datasets and unseen
manipulations, demonstrate that the proposed tagging approach achieves
excellent performance in the aspects of both authenticity verification and
source tracing for reliable fake news detection and outperforms the prior
works
Secure Hot Path Crowdsourcing with Local Differential Privacy under Fog Computing Architecture
Crowdsourcing plays an essential role in the Internet of Things (IoT) for
data collection, where a group of workers is equipped with Internet-connected
geolocated devices to collect sensor data for marketing or research purpose. In
this paper, we consider crowdsourcing these worker's hot travel path. Each
worker is required to report his real-time location information, which is
sensitive and has to be protected. Encryption-based methods are the most direct
way to protect the location, but not suitable for resource-limited devices.
Besides, local differential privacy is a strong privacy concept and has been
deployed in many software systems. However, the local differential privacy
technology needs a large number of participants to ensure the accuracy of the
estimation, which is not always the case for crowdsourcing. To solve this
problem, we proposed a trie-based iterative statistic method, which combines
additive secret sharing and local differential privacy technologies. The
proposed method has excellent performance even with a limited number of
participants without the need of complex computation. Specifically, the
proposed method contains three main components: iterative statistics, adaptive
sampling, and secure reporting. We theoretically analyze the effectiveness of
the proposed method and perform extensive experiments to show that the proposed
method not only provides a strict privacy guarantee, but also significantly
improves the performance from the previous existing solutions.Comment: This paper appears in IEEE Transactions on Services Computing.
https://doi.org/10.1109/TSC.2020.303933
Dipping PLMs Sauce: Bridging Structure and Text for Effective Knowledge Graph Completion via Conditional Soft Prompting
Knowledge Graph Completion (KGC) often requires both KG structural and
textual information to be effective. Pre-trained Language Models (PLMs) have
been used to learn the textual information, usually under the fine-tune
paradigm for the KGC task. However, the fine-tuned PLMs often overwhelmingly
focus on the textual information and overlook structural knowledge. To tackle
this issue, this paper proposes CSProm-KG (Conditional Soft Prompts for KGC)
which maintains a balance between structural information and textual knowledge.
CSProm-KG only tunes the parameters of Conditional Soft Prompts that are
generated by the entities and relations representations. We verify the
effectiveness of CSProm-KG on three popular static KGC benchmarks WN18RR,
FB15K-237 and Wikidata5M, and two temporal KGC benchmarks ICEWS14 and
ICEWS05-15. CSProm-KG outperforms competitive baseline models and sets new
state-of-the-art on these benchmarks. We conduct further analysis to show (i)
the effectiveness of our proposed components, (ii) the efficiency of CSProm-KG,
and (iii) the flexibility of CSProm-KG.Comment: Accepted by ACL 2023 Findings, Long Pape
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