ライフサイエンスコーパス: federate

1 computational tools that will be useful for federating a bioinformatics infrastructure and the open

2 Federated across two ARD cohorts, we find that patients

3 sign using the TriNetX Analytics platform, a federated, aggregated electronic health record (EHR) res

4 parate genotype datasets can be combined for federated aggregation and secure computation of allele f

5 introduce SF-Relate, a practical and secure federated algorithm for identifying genetic relatives ac

6 itional diseases and populations, supporting federated analyses across diverse biobanks, and facilita

7 Opportunities of cross-cohort federated analyses are discussed.

8 We explored the feasibility of multicohort federated analyses by examining associations between 3 p

9 ble datasets, enabling disclosure-controlled federated analyses of standardized clinical data.

10 annot be shared within the Consortium (e.g., federated analyses).

11 Initial feasibility analyses, using a federated analysis approach, yielded expected associatio

12 nstrate the feasibility and reliability of a federated analysis approach.

13 The common data and federated analysis framework developed through the Exten

14 istries, demonstrating that DataSHIELD-based federated analysis is a valuable approach in dermatology

15 Using a federated analysis model, in this cross-sectional popula

16 nternational DataSHIELD set-up, allowing for federated analysis of harmonized data.

17 on the DataSHIELD platform that supports the federated analysis of sensitive individual-level data.

18 ese challenges by enabling privacy-protected federated analysis of sensitive omic data.

19 tacted and asked to locally perform a novel, federated analysis of their data for single hit (one HRC

20 MOLGENIS Armadillo: a lightweight server for federated analysis solutions such as DataSHIELD.

21 To enable federated analysis, data owners need a user-friendly way

22 Federated analysis-which involves remote data analysis w

23 Using a federated analytical tool (DataSHIELD), we fitted linear

24 We propose FAMHE, a novel federated analytics system that, based on multiparty hom

25 Federated and Continual Learning have emerged as promisi

26 We present sPLINK, a hybrid federated and user-friendly tool, which performs privacy

27 The federated approach (0.8579; 95% CI, 0.7693-0.9299) perfo

28 Notably, the federated approach performed significantly worse than th

29 rtain use cases, cross-cohort analysis using federated approaches across multiple environments.

30 Federated approaches are promising to alleviate the issu

31 edical Data Translator project is working to federate autonomous reasoning agents and knowledge provi

32 Here, we posit that it is important to federate both computational resources (CPU, GPU, FPGA, e

33 riations in federated learning, we propose a federated clustered multi-domain learning algorithm base

34 or example, genotype imputation services and federated collaborations genomic analysis.

35 Here, we present Swarm, a framework for federated computation that promotes minimal data motion

36 promote international collaboration through federated constructs such as the International Brain Ini

37 a few hours with the aide of national-scale federated cyberinfrastructure.

38 icenter data standardization and implemented federated data analysis platforms like DataSHIELD to per

39 The establishment of well-curated, federated data repositories will provide a means to pres

40 , we examine and compare the centralized and federated data sharing models through the prism of five

41 The method can easily be implemented in a federated data-sharing context which is illustrated on a

42 We have created a federated database for genome studies of Magnaporthe gri

43 Data were obtained from the national federated databases for COVID-19 vaccination, SARS-CoV-2

44 are now being enhanced with new systems for federated databases, database publication, and more auto

45 In order to share data between federated databases, protocols for the exchange mechanis

46 is challenge, including data warehousing and federated databasing.

47 flow, where all analyses occur entirely in a federated environment subject to rigorous disclosure con

48 the BBMRI-ERIC Colorectal Cancer Cohort, the federated European Genome-phenome Archive, the Observati

49 Motivated by these, we developed FedGMMAT, a federated genetic association testing tool that utilizes

50 We introduce secure federated genome-wide association studies (SF-GWAS), a c

51 recent inclusion of web services designed to federate genomic data resources allows the information o

52 e usage of COLLAGENE by building a practical federated GWAS protocol for binary phenotypes and a secu

53 Here, we introduce Federated Harmony, a novel method combining properties o

54 The TriNetX Analytics platform, a federated health research network of aggregated deidenti

55 from the TriNetX Analytics Network, a global federated health research network of approximately 105.3

56 evel analysis of data from TriNetX, a global federated health research network, revealed that AA AD p

57 for real-time pancreatic cancer detection in federated healthcare environments.

58 ns to Galaxy's framework include support for federated identity and access management and increased a

59 Large-scale federated informatics approaches provide the ability to

60 ners need a user-friendly way to install the federated infrastructure and manage users and data.

61 es for Type 2 Diabetes Mellitus) network, 10 federated international data sources were included, span

62 At the heart of the caBIG approach to federated interoperability effort is a Grid middleware i

63 We present a secure federated kinship estimation framework and implement a s

64 er, an application that can query a virtual, federated knowledge graph derived from the aggregated in

65 o the widespread FL process, namely flexible federated learning (FFL) for collaborative training on s

66 Federated learning (FL) algorithms have been recently de

67 Split learning (SL) and Federated Learning (FL) are two effective learning appro

68 Federated learning (FL) emerges as a solution by enablin

69 Purpose To develop and optimize a federated learning (FL) framework across multiple client

70 edGlu, a machine learning model trained in a federated learning (FL) framework.

71 Federated learning (FL) has emerged as a solution, enabl

72 Federated learning (FL) is a method used for training ar

73 Federated Learning (FL) offers a potential solution by e

74 growing Internet of Things (IoT) landscape, federated learning (FL) plays a crucial role in enhancin

75 methods, such as centralized data sharing or federated learning (FL), face challenges, including priv

76 these data is not feasible with conventional federated learning (FL).

77 lti-Edge Clustered and Edge AI Heterogeneous Federated Learning (MEC-AI HetFL), which leverages multi

78 ly, there has been growing interest in Split Federated Learning (SFL), which combines elements of bot

79 he impact of data poisoning attacks on Split Federated Learning (SFL).

80 Split Federated Learning (SplitFed) has emerged as a decentral

81 challenge is a competition to benchmark (i) federated learning aggregation algorithms and (ii) state

82 We show that federated learning among 10 institutions results in mode

83 In essentials, Cedar integrates federated learning and meta-learning to enable a safegua

84 y for mitigating biases via disentanglement, federated learning and model explainability, and their r

85 Currently available methodologies, such as federated learning and swarm learning, have demonstrated

86 Here we present a completely decentralized federated learning approach, using knowledge distillatio

87 cussed for a future crop-yield decentralised federated learning architecture.

88 We discuss deep generative models and federated learning as strategies to augment datasets for

89 inates a significant limitation of canonical federated learning by allowing model heterogeneity; each

90 unication-efficient scheme for decentralized federated learning called ProxyFL, or proxy-based federa

91 nd the artificial intelligence (AI) approach federated learning can facilitate decentralized and harm

92 Federated learning can improve privacy protection in AI-

93 rming the siloed ones, and equivalent to the federated learning counterparts, but with increased sync

94 ensuring training integrity in decentralized federated learning environments, particularly in scenari

95 e study of applying a differentially private federated learning framework for analysis of histopathol

96 Moreover, implementing models within a federated learning framework not only ensures better pre

97 Here, we introduce scFed, a unified federated learning framework that allows for benchmarkin

98 This study introduces a federated learning framework that integrates RegNetZ and

99 ation methods, multi-modal data sources, and federated learning frameworks to enhance the model's gen

100 Decentralised federated learning has been proposed as a solution to ad

101 To address this concern, decentralized federated learning has been proposed, where classifier d

102 Decentralized Federated Learning improves data privacy and eliminates

103 We compared D-CLEF with centralized/siloed/federated learning in horizontal or vertical scenarios.

104 e review was conducted on the application of Federated Learning in training AI models for glaucoma sc

105 Federated learning is a distributed learning framework t

106 Federated learning is a novel paradigm for data-private

107 r work indicates that differentially private federated learning is a viable and reliable framework fo

108 ndings of this diagnostic study suggest that federated learning is a viable approach for the binary c

109 This proof of concept study, in which federated learning is applied to real-world datasets, pa

110 Clinical adoption of federated learning is expected to lead to models trained

111 However, the performance of federated learning is significantly influenced by the nu

112 Despite its potential, federated learning is still in its early stages of devel

113 Federated learning may represent a method to standardize

114 Federated learning offers a privacy-preserving solution

115 Federated Learning presents a promising strategy to over

116 robust approach to safeguard the distributed Federated Learning process in collaborative healthcare,

117 Federated learning provides a solution, allowing efficie

118 Data-centric, cross-silo federated learning represents a pathway forward for trai

119 e the impacts of adversarial misconduct in a Federated Learning scenario.

120 ulticentric brain tumor dataset in realistic federated learning simulations, yielding benefits for ad

121 itations, we develop a method that leverages federated learning to enable inverse probability of trea

122 as data encryption, differential privacy and federated learning to ensure the protection of neurodata

123 Essential strategies include employing federated learning to leverage distributed data, impleme

124 d long-tailed distributions, and it supports federated learning to preserve privacy.

125 stigates the impact of aggregation levels on federated learning using a proxy model trained on crop t

126 gy consumption by >4.5x compared to standard federated learning with a slight accuracy loss up to 1.5

127 mony, a novel method combining properties of federated learning with Harmony algorithm to integrate d

128 l identification methods but also highlights federated learning's potential for privacy-preserving, c

129 The DDT methodology is based on the idea of federated learning, a subfield of machine learning that

130 approaches demonstrating the superiority of federated learning, and discuss practical implementation

131 s such as seeking ethical approval, adopting federated learning, and following guidelines can address

132 ew of federated learning, privacy-preserving federated learning, and uncertainty quantification in fe

133 on, Synthetic Data, Semisupervised Learning, Federated Learning, Few-Shot Learning, Class Imbalance.

134 we conducted a multicentric TNBC study using federated learning, in which patient data remain secured

135 review provides a comprehensive overview of federated learning, privacy-preserving federated learnin

136 Keywords: Federated Learning, Prostate Cancer, MRI, Cancer Detecti

137 In federated learning, this task is particularly challengin

138 To address intra-client variations in federated learning, we propose a federated clustered mul

139 ckle this problem, researchers have proposed federated learning, where end-point users collaborativel

140 Xplore with keywords including "glaucoma," "federated learning," "artificial intelligence," "deep le

141 h other privacy-enhancing approaches-such as federated learning-analyses performed on synthetic data

142 ated learning called ProxyFL, or proxy-based federated learning.

143 the optimal aggregation levels for effective federated learning.

144 rning whereas 0.714 (95% CI 0.692-0.736) for federated learning.

145 learning, and uncertainty quantification in federated learning.

146 lyse its performance and is compared against federated learning.

147 a central coordinator, thereby going beyond federated learning.

148 (inter-client), degrades the performance of federated learning.

149 ailored, pretrained model architectures, and federated machine learning methods; 4, machine learning

150 eling using dsMTL outperformed conventional, federated machine learning, as well as the aggregation o

151 asing number of multi-party algorithms, e.g. federated machine learning, require the collaboration of

152 atiotemporal systems may be made possible by federated machine learning.

153 he state-of-the-art workflow limma voom in a federated manner, i.e., patient data never leaves its so

154 applied in a modular fashion for multiparty federated mega-analyses where the parties first agree to

155 ts reveal the feasibility and robust-ness of federated meta-learning in orchestrating heterogeneous r

156 Federated ML (FL) provides an alternative paradigm for a

157 ric, single-arm diagnostic study developed a federated model for melanoma-nevus classification using

158 This federated model for the legumes is managed as part of th

159 uce Distributed Cross-Learning for Equitable Federated models (D-CLEF), which incorporates horizontal

160 e, harmonization and interoperability, while federated models facilitate scaling up and legal complia

161 Our results showed that federated models generally achieved comparable or superi

162 In addition, federated models reached a robust generalization to inde

163 EAT Registry Taskforce developed a 5-country federated network among European atopic dermatitis regis

164 ronic health records from the TriNetX global federated network between December 2019 and June 2024.

165 eatments for atopic dermatitis by creating a federated network between national registries that enabl

166 earch in Therapeutics Network (DARTNet) is a federated network of electronic health data from 8 organ

167 ion System (TIES) Cancer Research Network, a federated network that facilitates data and biospecimen

168 As a result, federated networks have arisen, which allow simultaneous

169 This paper proposes a lead federated neuromorphic learning (LFNL) technique, which

170 re researchers to share code and datasets or federate privacy-preserving data to create open foundati

171 These findings highlight the potential of a federated privacy-preserving framework to avoid centrali

172 COVID-19 KG via a SPARQL endpoint to support federated queries on the Semantic Web and developed a kn

173 We demonstrate how federated queries that combine the Rhea SPARQL endpoint

174 e graphs for data integration, retrieval and federated queries.

175 ical databases has led to a growing trend to federate rather than duplicate them.

176 We used a multicenter federated research network to compare clinical outcomes

177 code, we hope it will foster the creation of federated research networks and thus accelerate drug dev

178 cluded training and optimizing local epochs, federated rounds, and aggregation strategies for FL-base

179 ber of participating entities, the number of federated rounds, and the selection algorithms.

180 ported by an integrated data system allowing federated search across several public bioinformatics re

181 Examples of federated search across the network illustrate the poten

182 asoning engine-supporting that language-that federates semantically integrated knowledge-bases.

183 To this end, we proposed a federated semi-supervised learning framework for automat

184 y used method from continual learning in the federated setting and observe this reoccurring phenomeno

185 oduce two published centralized studies in a federated setting, enabling biomedical insights that are

186 ntifiers introduces a barrier when executing federated SPARQL queries across life science data.

187 Here, we present an end-edge-cloud federated split learning framework to enable collaborati

188 High volcanic islands in the Federated States of Micronesia (Pohnpei and Kosrae) also

189 s, and Guam) and 3 freely associated states (Federated States of Micronesia [FSM], Republic of the Ma

190 an isolated population that is from Kosrae, Federated States of Micronesia and has abundant IBD, and

191 cycle in Africa to cause an outbreak in Yap, Federated States of Micronesia in 2007, French Polynesia

192 In 2007, an outbreak in the Federated States of Micronesia sparked public health con

193 0 individuals (Samoa) to 36,256 individuals (Federated States of Micronesia), equivalent to ~5% of hy

194 n in Tonga and in women in Kuwait, Kiribati, Federated States of Micronesia, Libya, Qatar, Tonga, and

195 ky sharks, with high mortality levels in the Federated States of Micronesia, Palau, and the Marshall

196 f mild febrile illness in 2007 in Yap State, Federated States of Micronesia.

197 y isolated population, the Island of Kosrae, Federated States of Micronesia.

198 tic association testing tool that utilizes a federated statistical testing approach for efficient ass

199 the open research challenges that we see in federating such infrastructures.

200 aims to explore the feasibility of a simple federated surveillance approach.

201 ndependent computational entities to lightly federate their capabilities as desired while maintaining

202 nless more countries invest in operating and federating their own open data resources.

203 f Health project, we are taking steps toward federating this infrastructure.

204 Federated training across many biobanks and clinical tri

205 Our platform orchestrates federated training for joint tumor classification and se

206 Federated training predicated on purifying hyperselectio

207 To this end, the Federated Tumor Segmentation (FeTS) Challenge represents

208 Segmentation masks were obtained via the federated tumor segmentation tool or the original data s