Filters








1,114 Hits in 4.2 sec

CAFE: Catastrophic Data Leakage in Vertical Federated Learning [article]

Xiao Jin, Pin-Yu Chen, Chia-Yi Hsu, Chia-Mu Yu, Tianyi Chen
2022 arXiv   pre-print
We name our proposed method as catastrophic data leakage in vertical federated learning (CAFE).  ...  Recent studies show that private training data can be leaked through the gradients sharing mechanism deployed in distributed machine learning systems, such as federated learning (FL).  ...  C-Y Hsu and C-M Yu were supported by MOST 110-2636-E-009-018, and we also thank National Center for High-performance Computing (NCHC) of National Applied Research Laboratories (NARLabs) in Taiwan for providing  ... 
arXiv:2110.15122v4 fatcat:ujhfouljx5crveigoxyeuynona

Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directions [article]

Qiang Duan, Shijing Hu, Ruijun Deng, Zhihui Lu
2022 arXiv   pre-print
Federated learning enables machine learning (ML) models locally trained using private data to be aggregated into a global model.  ...  In this article, we review the latest developments in federated learning and split learning and present a survey on the state-of-the-art technologies for combining these two learning methods in an edge  ...  The Multi-Vertical Federated Learning (Multi-VFL) framework proposed in [84] aims to enable collaborative learning in such scenarios with multiple data owners and label owners.  ... 
arXiv:2207.09611v1 fatcat:q3yukahrlnguflkbo66np3mtye

DVFL: A Vertical Federated Learning Method for Dynamic Data [article]

Yuzhi Liang, Yixiang Chen
2021 arXiv   pre-print
To alleviate this problem, we propose a new vertical federation learning method, DVFL, which adapts to dynamic data distribution changes through knowledge distillation.  ...  This paper studies vertical federated learning (VFL), which tackles the scenarios where collaborating organizations share the same set of users but disjoint features.  ...  CONCLUSION This paper proposes Dynamic Vertical Federated Learning (DVFL), a vertical federated learning method for dynamic data.  ... 
arXiv:2111.03341v1 fatcat:we37mirbwjfapew4qhht735sfe

Federated Learning on Non-IID Data: A Survey [article]

Hangyu Zhu, Jinjin Xu, Shiqing Liu, Yaochu Jin
2021 arXiv   pre-print
In this survey, we pro-vide a detailed analysis of the influence of Non-IID data on both parametric and non-parametric machine learning models in both horizontal and vertical federated learning.  ...  However, models trained in federated learning usually have worse performance than those trained in the standard centralized learning mode, especially when the training data are not independent and identically  ...  Vertical Federated Learning Vertical FL [150, 88] is also called heterogeneous FL [157] , in which users' training data share the same sample space but have different feature spaces.  ... 
arXiv:2106.06843v1 fatcat:qsfetsjmxrb6zhhuesgxcjuxj4

Privacy and Robustness in Federated Learning: Attacks and Defenses [article]

Lingjuan Lyu, Han Yu, Xingjun Ma, Chen Chen, Lichao Sun, Jun Zhao, Qiang Yang, Philip S. Yu
2022 arXiv   pre-print
Recently, federated learning (FL) has emerged as an alternative solution and continue to thrive in this new reality.  ...  Finally, we discuss promising future research directions towards robust and privacy-preserving federated learning.  ...  Another recent work [182] uncovered the risk of catastrophic data leakage in vertical federated learning (CAFE) through a novel algorithm that can perform largebatch data leakage with high data recovery  ... 
arXiv:2012.06337v3 fatcat:f5aflxnsdrdcdf4kvoa6yzseqq

A Systematic Literature Review on Federated Learning: From A Model Quality Perspective [article]

Yi Liu, Li Zhang, Ning Ge, Guanghao Li
2020 arXiv   pre-print
As an emerging technique, Federated Learning (FL) can jointly train a global model with the data remaining locally, which effectively solves the problem of data privacy protection through the encryption  ...  learning quality.  ...  There are three major types of federated settings regarding the data features in FL as shown in Fig. 2 [15] , i.e., horizontal federated learning, vertical federated learning, and federated transfer  ... 
arXiv:2012.01973v1 fatcat:64yt53gdavfavmi5puj4rflknm

Towards Deep Federated Defenses Against Malware in Cloud Ecosystems [article]

Josh Payne, Ashish Kundu
2019 arXiv   pre-print
With this foundation, we consider the multicloud case, in which multiple clouds with differing privacy requirements cooperate against the spread of malware, proposing the use of federated learning to perform  ...  for response strategy, action spaces for malware containment and eradication, and developing priors and transfer learning tasks for machine learning models in this area.  ...  From a customer's standpoint, this kind of incident can be catastrophic, as malware attacks often lead to the leakage of sensitive data and/or extended downtime of services.  ... 
arXiv:1912.12370v1 fatcat:cbleywzls5g4hm4wbuyipj5u2a

Federated Learning for IoUT: Concepts, Applications, Challenges and Opportunities [article]

Nancy Victor, Rajeswari. C, Mamoun Alazab, Sweta Bhattacharya, Sindri Magnusson, Praveen Kumar Reddy Maddikunta, Kadiyala Ramana, Thippa Reddy Gadekallu
2022 arXiv   pre-print
Federated learning (FL) is a secured, decentralized framework which is a recent development in machine learning, that will help in fulfilling the challenges faced by conventional ML approaches in IoUT.  ...  However, an extensive review of the various studies conducted highlight the significance of data privacy and security in IoUT frameworks as a predominant factor in achieving desired outcomes in mission  ...  The rig was sunk and resulted in a catastrophic oil leakage from the well, and has has affected nearly 70000 square miles of ocean in the Gulf of Mexico.  ... 
arXiv:2207.13976v1 fatcat:3ttovdqujzaypiaicctkgsypwm

Federated Horizontally Partitioned Principal Component Analysis for Biomedical Applications

Anne Hartebrodt, Richard Röttger, Thomas Lengauer
2022 Bioinformatics Advances  
Motivation Federated learning enables privacy preserving machine learning in the medical domain because the sensitive patient data remains with the owner and only parameters are exchanged between the data  ...  step in machine learning and visualization workflows.  ...  These scenarios are cases of cross-silo federated learning where larger chunks of data are stored in "data silos".  ... 
doi:10.1093/bioadv/vbac026 fatcat:enq6nf3hcfgwvmactbec56lzve

Towards Fair Federated Learning with Zero-Shot Data Augmentation [article]

Weituo Hao, Mostafa El-Khamy, Jungwon Lee, Jianyi Zhang, Kevin J Liang, Changyou Chen, Lawrence Carin
2021 arXiv   pre-print
In this work, we aim to provide federated learning schemes with improved fairness.  ...  We study two variants of this scheme, Fed-ZDAC (federated learning with zero-shot data augmentation at the clients) and Fed-ZDAS (federated learning with zero-shot data augmentation at the server).  ...  To ensure federated learning approaches satisfy differential privacy, the work in [8] proposed a client level perspective by adding Gaussian noise to the model update, which can prevent the leakage of  ... 
arXiv:2104.13417v1 fatcat:ycavxam7kne3vmj7cmijglx25i

Applying Federated Learning in Software-Defined Networks: A Survey

Xiaohang Ma, Lingxia Liao, Zhi Li, Roy Xiaorong Lai, Miao Zhang
2022 Symmetry  
Federated learning (FL) is a type of distributed machine learning approacs that trains global models through the collaboration of participants.  ...  It protects data privacy as participants only contribute local models instead of sharing private local data.  ...  Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/sym14020195 doaj:dc5c07021da046248650414cb605c7f7 fatcat:tuelywpsyjdhlo3soyirrvjy6i

Precision Health Data: Requirements, Challenges and Existing Techniques for Data Security and Privacy [article]

Chandra Thapa, Seyit Camtepe
2020 arXiv   pre-print
., machine learning), and communication (e.g., interaction between the health data centers).  ...  Secondly, this paper investigates secure and privacy-preserving machine learning methods suitable for the computation of precision health data along with their usage in relevant health projects.  ...  Federated Learning and healthcare: FL has a vast application in health data analytics.  ... 
arXiv:2008.10733v1 fatcat:oj2neoftf5hcbpatnfn7ntyhzy

HAFLO: GPU-Based Acceleration for Federated Logistic Regression [article]

Xiaodian Cheng, Wanhang Lu, Xinyang Huang, Shuihai Hu, Kai Chen
2021 arXiv   pre-print
In recent years, federated learning (FL) has been widely applied for supporting decentralized collaborative learning scenarios.  ...  Among existing FL models, federated logistic regression (FLR) is a widely used statistic model and has been used in various industries.  ...  ., 2016] , Federated Learning (FL) [Konečnỳ et al., 2016] , etc. In the area of multi-party collaborative learning, FL is the most promising technique.  ... 
arXiv:2107.13797v3 fatcat:5idrrfy5mrbhlllkwm2doadno4

Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review [article]

Leon Witt, Mathis Heyer, Kentaroh Toyoda, Wojciech Samek, Dan Li
2022 arXiv   pre-print
Yet, in order to scale this new paradigm beyond small groups of already entrusted entities towards mass adoption, the Federated Learning Framework (FLF) has to become (i) truly decentralized and (ii) participants  ...  This is the first systematic literature review analyzing holistic FLFs in the domain of both, decentralized and incentivized federated learning. 422 publications were retrieved, by querying 12 major scientific  ...  This problem occurs for vertical federated learning where different parties hold complementary information about the same user.  ... 
arXiv:2205.07855v2 fatcat:ds2wavc33nd5jdw462l2vuddya

Blockchain-Based Federated Learning in UAVs Beyond 5G Networks: A Solution Taxonomy and Future Directions

Deepti Saraswat, Ashwin Verma, Pronaya Bhattacharya, Sudeep Tanwar, Gulshan Sharma, Pitshou N. Bokoro, Ravi Sharma
2022 IEEE Access  
Federated learning (FL) allows data to be trained on local nodes, preserving privacy and improving network communication.  ...  INDEX TERMS Beyond 5G networks, 6G, blockchain, federated learning, unmanned aerial vehicles. D. ORGANIZATION AND READING MAP  ...  to notify in case of leakages.  ... 
doi:10.1109/access.2022.3161132 fatcat:4h6ormfvjfd45n25vvzkynvyg4
« Previous Showing results 1 — 15 out of 1,114 results