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Cross-Silo Federated Learning for Multi-Tier Networks with Vertical and Horizontal Data Partitioning [article]

Anirban Das, Timothy Castiglia, Shiqiang Wang, Stacy Patterson
2021 arXiv   pre-print
We consider federated learning in tiered communication networks. Our network model consists of a set of silos, each holding a vertical partition of the data.  ...  Each silo contains a hub and a set of clients, with the silo's vertical data shard partitioned horizontally across its clients.  ...  ), and by the National Science Foundation under grants CNS 1553340 and CNS 1816307.  ... 
arXiv:2108.08930v2 fatcat:qotfeu4ufvgi3ojy43lg62lgse

Multi-Tier Federated Learning for Vertically Partitioned Data [article]

Anirban Das, Stacy Patterson
2021 arXiv   pre-print
Each silo contains a hub and a set of clients, with the silo's vertical data shard partitioned horizontally across its clients.  ...  We consider decentralized model training in tiered communication networks. Our network model consists of a set of silos, each holding a vertical partition of the data.  ...  Our approach is thus a novel combination of learning with both vertically and horizontally partitioned data in a multi-tiered network.  ... 
arXiv:2102.03620v1 fatcat:7j4rufuzefay5dz3ekzudwwjwy

A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection [article]

Qinbin Li, Zeyi Wen, Zhaomin Wu, Sixu Hu, Naibo Wang, Yuan Li, Xu Liu, Bingsheng He
2021 arXiv   pre-print
Moreover, we provide a thorough categorization for federated learning systems according to six different aspects, including data distribution, machine learning model, privacy mechanism, communication architecture  ...  As researchers try to support more machine learning models with different privacy-preserving approaches, there is a requirement in developing systems and infrastructures to ease the development of various  ...  Acknowledgement This work is supported by a MoE AcRF Tier 1 grant (T1 251RES1824), an SenseTime Young Scholars Research Fund, and a MOE Tier 2 grant (MOE2017-T2-1-122) in Singapore.  ... 
arXiv:1907.09693v6 fatcat:d3l2l664mjdfrjgyok43pfxnvq

Management of Resource at the Network Edge for Federated Learning [article]

Silvana Trindade, Luiz F. Bittencourt, Nelson L. S. da Fonseca
2022 arXiv   pre-print
Federated learning has been explored as a promising solution for training at the edge, where end devices collaborate to train models without sharing data with other entities.  ...  In this paper, we describe the recent work on resource management at the edge, and explore the challenges and future directions to allow the execution of federated learning at the edge.  ...  Acknowledgments This work was supported by CAPES, CNPq, and grant 15/24494-8, FAPESP, BRAZIL.  ... 
arXiv:2107.03428v2 fatcat:hez3rqjonzd45plyvdzujdjt6u

System Optimization in Synchronous Federated Training: A Survey [article]

Zhifeng Jiang, Wei Wang
2021 arXiv   pre-print
The unprecedented demand for collaborative machine learning in a privacy-preserving manner gives rise to a novel machine learning paradigm called federated learning (FL).  ...  for contrasting factors, client heterogeneity, and huge configuration space.  ...  horizontal FL, vertical FL and federated transfer learning; and (3) it allows customization on the FL pipeline such as the aggregation step.  ... 
arXiv:2109.03999v2 fatcat:oxmq44iuo5eexbjtq7xdj3quq4

A Systematic Literature Review on Federated Machine Learning: From A Software Engineering Perspective [article]

Sin Kit Lo, Qinghua Lu, Chen Wang, Hye-Young Paik, Liming Zhu
2021 arXiv   pre-print
Our data synthesis covers the lifecycle of federated learning system development that includes background understanding, requirement analysis, architecture design, implementation, and evaluation.  ...  Federated learning is an emerging machine learning paradigm where clients train models locally and formulate a global model based on the local model updates.  ...  (3%) Cross-device Cross-silo Both 11 1 3 Data partitioning (<1%) Horizontal federated learning Vertical federated learning 1 1 Table 10 . 10 Data Types and Applications Distribution  ... 
arXiv:2007.11354v8 fatcat:4aokqyvrqrbpzpsptjbsipeyou

Aligning a Multi-Government Network With Situational Context

Herman L. Boschken
2016 American Review of Public Administration  
It illustrates the logic by introducing a toolbox for multi-government design that speaks to the adaptive qualities of government networks in whole metropolitan areas.  ...  The governance of major metropolitan areas is often associated with a "fragmented" and "uncoordinated" multi-government apparatus, frequently sculpted from years of particularistic ad hoc administrative  ...  Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.  ... 
doi:10.1177/0275074016668402 fatcat:ajuv6f5sarekzfubbfunng3dqe

Privacy-aware Resource sharing in Cross-device Federated Model Training for Collaborative Predictive Maintenance

Sourabh Bharti, Alan McGibney
2021 IEEE Access  
Federated Learning (FL) has been explored to address these challenges and has been demonstrated to provide a mechanism that allows highly distributed data to be mined in a privacy-preserving manner and  ...  INDEX TERMS SplitNN, federated learning, predictive maintenance, Industry 4.0.  ...  This work considers a cross-device FL setting with asset failure data horizontally partitioned across multiple IoT edge devices deployed at different manufacturing sites. B.  ... 
doi:10.1109/access.2021.3108839 fatcat:qadewavoqjflbaf2huh3ymiwfu

A Survey of Federated Learning for Edge Computing: Research Problems and Solutions

Qi Xia, Winson Ye, Zeyi Tao, Jindi Wu, Qun Li
2021 High-Confidence Computing  
Federated learning is well suited for edge computing applications and can leverage the the computation power of edge servers and the data collected on widely dispersed edge devices.  ...  Federated Learning is a machine learning scheme in which a shared prediction model can be collaboratively learned by a number of distributed nodes using their locally stored data.  ...  For example, to avoid vertical and horizontal heterogeneity, the program contains specialized nodes developed by domain experts that can only be wired together with specific nodes.  ... 
doi:10.1016/j.hcc.2021.100008 fatcat:fzzqredg6nfsxg6wlu5h7chixq

Resilient Cyberphysical Systems and their Application Drivers: A Technology Roadmap [article]

Somali Chaterji, Parinaz Naghizadeh, Muhammad Ashraful Alam, Saurabh Bagchi, Mung Chiang, David Corman, Brian Henz, Suman Jana, Na Li, Shaoshuai Mou, Meeko Oishi, Chunyi Peng (+5 others)
2019 arXiv   pre-print
municipal electrical grids and other connected infrastructures, data breaches, and network failures; and the fragility of engineered designs themselves encompassing bugs, human-computer interactions (HCI  ...  An optimally designed system is resilient to both unique attacks and recurrent attacks, the latter with a lower overhead.  ...  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.  ... 
arXiv:2001.00090v1 fatcat:zaybw5wyfbayhcu34q2douctme

The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning [article]

Raed Kontar, Naichen Shi, Xubo Yue, Seokhyun Chung, Eunshin Byon, Mosharaf Chowdhury, Judy Jin, Wissam Kontar, Neda Masoud, Maher Noueihed, Chinedum E. Okwudire, Garvesh Raskutti (+3 others)
2021 arXiv   pre-print
This article provides a vision for IoFT and a systematic overview of current efforts towards realizing this vision.  ...  This paradigm shift was set into motion by the tremendous increase in computational power on IoT devices and the recent advances in decentralized and privacy-preserving model training, coined as federated  ...  Federated multi-task learning. Con- without data sharing.  ... 
arXiv:2111.05326v1 fatcat:bbgdhtuqcrhstgakt2vxuve2ca

Seeing the forest for the trees: Visualizing platformization and its governance

José Van Dijck
2020 New Media & Society  
The layered shape of the tree draws attention to the dynamics of power concentration: vertical integration, infrastructuralization, and cross-sectorization.  ...  The complexities of platforms are increasingly at odds with the narrow legal and economic concepts in which their governance is grounded.  ...  Poell, and David Nieborg for their critical readings and discussions.  ... 
doi:10.1177/1461444820940293 fatcat:gao5lzhx4vfs7dwjabmiojimg4

5GZORRO_D2.2: Design of the 5GZORRO Platform for Security & Trust

. Carrozzo, P.G. Giardina, J. Brenes, E. Bucchianeri, G. Landi, C. Herranz, A. Fernandez, M. S. Siddiqui, Javier Fernandez, K. Meth, K. Barabash, Diego R. López, P. Diogo, L. Conceicao, T. Subramanya, R. Behravesh, J.M. Jorquera Valero, P. M. Sánchez Sánchez, M. Gil Pérez, G. Martínez Pérez, J. Taylor, J. Bonnet, P. Chainho, M. Mertiri, T. Bozios, A. Lekidis, V. Theodorou, D. Laskaratos, F. Bravo Díaz, A. Ramos, J.M. Mifsud, A. Sciberreas
2021 Zenodo  
Horizontal and Vertical VNF Auto-scaling: Resource scaling could be either horizontal or vertical.  ...  Furthermore, we will use the Federated Learning approach to design the algorithms to preserve the privacy of user data considering the multi-domain nature of 5G networks and compare their performance against  ... 
doi:10.5281/zenodo.5561042 fatcat:o5ecnvwvifhmveigarvfgih3l4

The Future Internet convergence of IMS and ubiquitous smart environments: An IMS-based solution for energy efficiency

Paolo Bellavista, Giuseppe Cardone, Antonio Corradi, Luca Foschini
2012 Journal of Network and Computer Applications  
This thesis focuses on pervasive sensing systems to extract design guidelines as foundation of a comprehensive reference model for multi-tier Pervasive Sensing applications.  ...  , together with a direction for the design and deployment of future Pervasive Sensing applications.  ...  Usually, these are typically vertical "silos" applications that start from raw sensor data, go through a preprocessing stage, and end with a classification stage.  ... 
doi:10.1016/j.jnca.2011.05.003 fatcat:3iprgfdvmjfxxey67qtflilnri

On the Rollout of Network Slicing in Carrier Networks: A Technology Radar

Jose Ordonez-Lucena, Pablo Ameigeiras, Luis M. Contreras, Jesús Folgueira, Diego R. López
2021 Sensors  
These multi-technology, multi-vendor and brownfield features constitute a challenge for the operator, which is required to deploy and operate slices across all these domains in order to satisfy the end-to-end  ...  Network slicing is a powerful paradigm for network operators to support use cases with widely diverse requirements atop a common infrastructure.  ...  Besides operators, the outcomes of this work may be of relevance for other audience, including vendors, verticals and researchers.  ... 
doi:10.3390/s21238094 pmid:34884098 fatcat:p5bkzefifbetnfv2fozqjf4wvq
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