FedV (original) (raw)

FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data

James Joshi

arXiv (Cornell University), 2021

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FEDERATED LEARNING: TRAINING ML MODELS COLLABORATIVELY ACROSS MULTIPLE DEVICES WITHOUT SHARING RAW DATA, PRESERVING PRIVACY

Neha Joshi

Rabindra Bharati University Journal of Economics, 2024

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A Federated Learning Framework for Privacy-preserving and Parallel Training

Hien Tran

2020

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Efficient Privacy-Aware Federated Learning by Elimination of Downstream Redundancy

Shubham Rai

IEEE Design & Test, 2021

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Hercules: Boosting the Performance of Privacy-preserving Federated Learning

Xingshuo han

arXiv (Cornell University), 2022

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An Empirical Study of Efficiency and Privacy of Federated Learning Algorithms

Hajar Bennouri

arXiv (Cornell University), 2023

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Federated Learning with Privacy-preserving and Model IP-right-protection

lixin fan

Machine Intelligence Research

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Privacy Threats Analysis to Secure Federated Learning

Liyao Xiang

ArXiv, 2021

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Differentially Private Federated Learning for Bandwidth and Energy Constrained Environments. (Apprentissage fédéré avec confidentialité différentielle pour les environnements contraints en bande passante et énergie)

Raouf Kerkouche

2021

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Efficient and Private Federated Learning with Partially Trainable Networks

Ankush Garg

ArXiv, 2021

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BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning

Arup Mondal

2022

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Federated Learning using Distributed Messaging with Entitlements for Anonymous Computation and Secure Delivery of Model

Sudhir Upadhyay

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Secure Federated Learning with a Homomorphic Encryption Model

Mohammad Aljanabi

International Journal Papier Advance and Scientific Review

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Privacy and Efficiency of Communications in Federated Split Learning

Ibrahim Matta

arXiv (Cornell University), 2023

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Confidential Training and Inference using Secure Multi-Party Computation on Vertically Partitioned Dataset

Jossy P GEORGE

Scalable Computing: Practice and Experience

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Privacy-Preserving Distributed Support Vector Machines

Stefano Braghin

Heterogeneous Data Management, Polystores, and Analytics for Healthcare, 2021

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Privacy-preserving and bandwidth-efficient federated learning

Raouf Kerkouche

Proceedings of the Conference on Health, Inference, and Learning

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Privacy and Trust Redefined in Federated Machine Learning

Will Abramson

Machine Learning and Knowledge Extraction

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Trading Off Privacy, Utility and Efficiency in Federated Learning

Yan Kang

ACM Transactions on Intelligent Systems and Technology

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Privacy-Preserving Federated Learning via System Immersion and Random Matrix Encryption

haleh hayati

ArXiv, 2022

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A Survey on Securing Federated Learning: Analysis of Applications, Attacks, Challenges, and Trends

Helio Cunha

IEEE Access

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Confidential machine learning on untrusted platforms: a survey

keke chen

Cybersecurity, 2021

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No Peek: A Survey of private distributed deep learning

Praneeth Vepakomma

2018

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Secure and Efficient Federated Transfer Learning

Yan Kang

2019 IEEE International Conference on Big Data (Big Data), 2019

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Federated Learning is Better with Non-Homomorphic Encryption

Konstantin Burlachenko

2023

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Decentralized Machine Learning Models with Cryptographic Techniques

IJERA Journal

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Privacy and Security in Federated Learning: A Survey

alexandre benoit

Applied Sciences

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Towards Practical Privacy-Preserving Collaborative Machine Learning at a Scale

Rania Talbi

2020 50th Annual IEEE-IFIP International Conference on Dependable Systems and Networks-Supplemental Volume (DSN-S)

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Private Dataset Generation Using Privacy Preserving Collaborative Learning

amit chaulwar

2020

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Privacy-preserving federated learning for scalable and high data quality computational-intelligence-as-a-service in Society 5.0

Soodeh Peyvandi

Multimedia Tools and Applications, 2022

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Privacy-Preserving Federated Learning on Partitioned Attributes

Liyao Xiang

ArXiv, 2021

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Privacy-preserving Machine Learning in Cloud

Mehdi Ghasemi

Proceedings of the 2017 on Cloud Computing Security Workshop, 2017

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Security and privacy in federated learning: A survey

Kandati Dasaradharami Reddy, Anusha S

peertechz publications, 2023

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A federated deep learning framework for privacy preservation and communication efficiency

Hiền Trần

Journal of Systems Architecture, 2022

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