84 research outputs found
Swin Transformer-Based Dynamic Semantic Communication for Multi-User with Different Computing Capacity
Semantic communication has gained significant attention from researchers as a
promising technique to replace conventional communication in the next
generation of communication systems, primarily due to its ability to reduce
communication costs. However, little literature has studied its effectiveness
in multi-user scenarios, particularly when there are variations in the model
architectures used by users and their computing capacities. To address this
issue, we explore a semantic communication system that caters to multiple users
with different model architectures by using a multi-purpose transmitter at the
base station (BS). Specifically, the BS in the proposed framework employs
semantic and channel encoders to encode the image for transmission, while the
receiver utilizes its local channel and semantic decoder to reconstruct the
original image. Our joint source-channel encoder at the BS can effectively
extract and compress semantic features for specific users by considering the
signal-to-noise ratio (SNR) and computing capacity of the user. Based on the
network status, the joint source-channel encoder at the BS can adaptively
adjust the length of the transmitted signal. A longer signal ensures more
information for high-quality image reconstruction for the user, while a shorter
signal helps avoid network congestion. In addition, we propose a hybrid loss
function for training, which enhances the perceptual details of reconstructed
images. Finally, we conduct a series of extensive evaluations and ablation
studies to validate the effectiveness of the proposed system.Comment: 14 pages, 10 figure
An Efficient Federated Learning Framework for Training Semantic Communication System
Semantic communication has emerged as a pillar forthe next generation of communication systems due to its capabilities in alleviating data redundancy. Most semantic communication systems are built upon advanced deep learning models whosetraining performance heavily relies on data availability. Existingstudies often make unrealistic assumptions of a readily accessibledata source, where in practice, data is mainly created on the clientside. Due to privacy and security concerns, the transmission ofdata is restricted, which is necessary for conventional centralizedtraining schemes. To address this challenge, we explore semanticcommunication in a federated learning (FL) setting that utilizesclient data without leaking privacy. Additionally, we designour system to tackle the communication overhead by reducingthe quantity of information delivered in each global round.In this way, we can save significant bandwidth for resourcelimited devices and reduce overall network traffic. Finally, weintroduce a mechanism to aggregate the global model fromclients, called FedLol. Extensive simulation results demonstratethe effectiveness of our proposed technique compared to baselinemethods
Contrastive encoder pre-training-based clustered federated learning for heterogeneous data
Federated learning (FL) is a promising approach that enables distributed
clients to collaboratively train a global model while preserving their data
privacy. However, FL often suffers from data heterogeneity problems, which can
significantly affect its performance. To address this, clustered federated
learning (CFL) has been proposed to construct personalized models for different
client clusters. One effective client clustering strategy is to allow clients
to choose their own local models from a model pool based on their performance.
However, without pre-trained model parameters, such a strategy is prone to
clustering failure, in which all clients choose the same model. Unfortunately,
collecting a large amount of labeled data for pre-training can be costly and
impractical in distributed environments. To overcome this challenge, we
leverage self-supervised contrastive learning to exploit unlabeled data for the
pre-training of FL systems. Together, self-supervised pre-training and client
clustering can be crucial components for tackling the data heterogeneity issues
of FL. Leveraging these two crucial strategies, we propose contrastive
pre-training-based clustered federated learning (CP-CFL) to improve the model
convergence and overall performance of FL systems. In this work, we demonstrate
the effectiveness of CP-CFL through extensive experiments in heterogeneous FL
settings, and present various interesting observations.Comment: Published in Neural Network
Federated Learning with Intermediate Representation Regularization
In contrast to centralized model training that involves data collection,
federated learning (FL) enables remote clients to collaboratively train a model
without exposing their private data. However, model performance usually
degrades in FL due to the heterogeneous data generated by clients of diverse
characteristics. One promising strategy to maintain good performance is by
limiting the local training from drifting far away from the global model.
Previous studies accomplish this by regularizing the distance between the
representations learned by the local and global models. However, they only
consider representations from the early layers of a model or the layer
preceding the output layer. In this study, we introduce FedIntR, which provides
a more fine-grained regularization by integrating the representations of
intermediate layers into the local training process. Specifically, FedIntR
computes a regularization term that encourages the closeness between the
intermediate layer representations of the local and global models.
Additionally, FedIntR automatically determines the contribution of each layer's
representation to the regularization term based on the similarity between local
and global representations. We conduct extensive experiments on various
datasets to show that FedIntR can achieve equivalent or higher performance
compared to the state-of-the-art approaches. Our code is available at
https://github.com/YLTun/FedIntR.Comment: IEEE BigComp 202
Federated Learning with Diffusion Models for Privacy-Sensitive Vision Tasks
Diffusion models have shown great potential for vision-related tasks,
particularly for image generation. However, their training is typically
conducted in a centralized manner, relying on data collected from publicly
available sources. This approach may not be feasible or practical in many
domains, such as the medical field, which involves privacy concerns over data
collection. Despite the challenges associated with privacy-sensitive data, such
domains could still benefit from valuable vision services provided by diffusion
models. Federated learning (FL) plays a crucial role in enabling decentralized
model training without compromising data privacy. Instead of collecting data,
an FL system gathers model parameters, effectively safeguarding the private
data of different parties involved. This makes FL systems vital for managing
decentralized learning tasks, especially in scenarios where privacy-sensitive
data is distributed across a network of clients. Nonetheless, FL presents its
own set of challenges due to its distributed nature and privacy-preserving
properties. Therefore, in this study, we explore the FL strategy to train
diffusion models, paving the way for the development of federated diffusion
models. We conduct experiments on various FL scenarios, and our findings
demonstrate that federated diffusion models have great potential to deliver
vision services to privacy-sensitive domains
Mako: a graph-based pattern growth approach to detect complex structural variants
Computer Systems, Imagery and Medi
Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis
BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London
Post-intervention Status in Patients With Refractory Myasthenia Gravis Treated With Eculizumab During REGAIN and Its Open-Label Extension
OBJECTIVE: To evaluate whether eculizumab helps patients with anti-acetylcholine receptor-positive (AChR+) refractory generalized myasthenia gravis (gMG) achieve the Myasthenia Gravis Foundation of America (MGFA) post-intervention status of minimal manifestations (MM), we assessed patients' status throughout REGAIN (Safety and Efficacy of Eculizumab in AChR+ Refractory Generalized Myasthenia Gravis) and its open-label extension. METHODS: Patients who completed the REGAIN randomized controlled trial and continued into the open-label extension were included in this tertiary endpoint analysis. Patients were assessed for the MGFA post-intervention status of improved, unchanged, worse, MM, and pharmacologic remission at defined time points during REGAIN and through week 130 of the open-label study. RESULTS: A total of 117 patients completed REGAIN and continued into the open-label study (eculizumab/eculizumab: 56; placebo/eculizumab: 61). At week 26 of REGAIN, more eculizumab-treated patients than placebo-treated patients achieved a status of improved (60.7% vs 41.7%) or MM (25.0% vs 13.3%; common OR: 2.3; 95% CI: 1.1-4.5). After 130 weeks of eculizumab treatment, 88.0% of patients achieved improved status and 57.3% of patients achieved MM status. The safety profile of eculizumab was consistent with its known profile and no new safety signals were detected. CONCLUSION: Eculizumab led to rapid and sustained achievement of MM in patients with AChR+ refractory gMG. These findings support the use of eculizumab in this previously difficult-to-treat patient population. CLINICALTRIALSGOV IDENTIFIER: REGAIN, NCT01997229; REGAIN open-label extension, NCT02301624. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that, after 26 weeks of eculizumab treatment, 25.0% of adults with AChR+ refractory gMG achieved MM, compared with 13.3% who received placebo
Minimal Symptom Expression' in Patients With Acetylcholine Receptor Antibody-Positive Refractory Generalized Myasthenia Gravis Treated With Eculizumab
The efficacy and tolerability of eculizumab were assessed in REGAIN, a 26-week, phase 3, randomized, double-blind, placebo-controlled study in anti-acetylcholine receptor antibody-positive (AChR+) refractory generalized myasthenia gravis (gMG), and its open-label extension
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