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Two-Phase Sparsification With Secure Aggregation for Privacy-Aware Federated Learning

IEEE internet of things journal, 2024-08, Vol.11 (16), p.27112-27125

2024

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NVAS: A non-interactive verifiable federated learning aggregation scheme for COVID-19 based on game theory

Computer communications, 2023-06, Vol.206, p.1-9

2023

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Top-k sparsification with secure aggregation for privacy-preserving federated learning

Computers & security, 2023-01, Vol.124, p.102993, Article 102993

2023

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CESA: Communication efficient secure aggregation scheme via sparse graph in federated learning

Journal of network and computer applications, 2024-11, Vol.231, p.103997, Article 103997

2024

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On the Information Theoretic Secure Aggregation With Uncoded Groupwise Keys

IEEE transactions on information theory, 2024-09, Vol.70 (9), p.6596-6619

2024

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Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning

IEEE journal on selected areas in information theory, 2021-03, Vol.2 (1), p.479-489

2021

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Privacy-preserving federated learning for residential short-term load forecasting

Applied energy, 2022-11, Vol.326, p.119915, Article 119915

2022

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Preventing harm to the rare in combating the malicious: A filtering-and-voting framework with adaptive aggregation in federated learning

Neurocomputing (Amsterdam), 2024-11, Vol.604, p.128317, Article 128317

2024

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The Capacity Region of Information Theoretic Secure Aggregation with Uncoded Groupwise Keys

IEEE transactions on information theory, 2024, p.1-1

2024

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HeteroSAg: Secure Aggregation With Heterogeneous Quantization in Federated Learning

IEEE transactions on communications, 2022-04, Vol.70 (4), p.2372-2386

2022

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Secure Gradient Aggregation with Sparsification for Resource-Limited Federated Learning

IEEE transactions on communications, 2024, p.1-1

2024

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SwiftAgg+: Achieving Asymptotically Optimal Communication Loads in Secure Aggregation for Federated Learning

IEEE journal on selected areas in communications, 2023-04, Vol.41 (4), p.1-1

2023

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Aggregation Service for Federated Learning: An Efficient, Secure, and More Resilient Realization

IEEE transactions on dependable and secure computing, 2023-03, Vol.20 (2), p.988-1001

2023

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Efficient privacy-preserving aggregation for demand side management of residential loads

Applied energy, 2022-12, Vol.328, p.120112, Article 120112

2022

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Verifiable Federated Learning With Privacy-Preserving Data Aggregation for Consumer Electronics

IEEE transactions on consumer electronics, 2024-02, Vol.70 (1), p.2696-2707

2024

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CORK: A privacy-preserving and lossless federated learning scheme for deep neural network

Information sciences, 2022-07, Vol.603, p.190-209

2022

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Blockchain-Based Efficiently Privacy-Preserving Federated Learning Framework Using Shamir Secret Sharing

IEEE transactions on consumer electronics, 2024-08, p.1-1

2024

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GoCrowd: Obliviously Aggregating Crowd Wisdom With Quality Awareness in Crowdsourcing

IEEE transactions on dependable and secure computing, 2024-06, p.1-13

2024

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Graph Sparsification-Based Secure Federated Learning for Consumer-Driven Internet of Things

IEEE transactions on consumer electronics, 2024-06, p.1-1

2024

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Secure Single-Server Aggregation with (Poly)Logarithmic Overhead

Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security, 2020, p.1253-1269

2020

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基于相似度聚类的可信联邦安全聚合算法

电子与信息学报, 2023-03, Vol.45 (3), p.894-904

2023

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On the Security of Verifiable and Oblivious Secure Aggregation for Privacy-Preserving Federated Learning

IEEE transactions on dependable and secure computing, 2024, Vol.21 (5), p.1-2

2024

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Malicious-Resistant Non-Interactive Verifiable Aggregation for Federated Learning

IEEE transactions on dependable and secure computing, 2024, p.1-17

2024

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MASKCRYPT: Federated Learning with Selective Homomorphic Encryption

IEEE transactions on dependable and secure computing, 2024, p.1-14

2024

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Information Theoretic Secure Aggregation With User Dropouts

IEEE transactions on information theory, 2022-11, Vol.68 (11), p.7471-7484

2022

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Two-Phase Sparsification With Secure Aggregation for Privacy-Aware Federated Learning

NVAS: A non-interactive verifiable federated learning aggregation scheme for COVID-19 based on game theory

Top-k sparsification with secure aggregation for privacy-preserving federated learning

CESA: Communication efficient secure aggregation scheme via sparse graph in federated learning

On the Information Theoretic Secure Aggregation With Uncoded Groupwise Keys

Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning

Privacy-preserving federated learning for residential short-term load forecasting

Preventing harm to the rare in combating the malicious: A filtering-and-voting framework with adaptive aggregation in federated learning

The Capacity Region of Information Theoretic Secure Aggregation with Uncoded Groupwise Keys

HeteroSAg: Secure Aggregation With Heterogeneous Quantization in Federated Learning

Secure Gradient Aggregation with Sparsification for Resource-Limited Federated Learning

SwiftAgg+: Achieving Asymptotically Optimal Communication Loads in Secure Aggregation for Federated Learning

Aggregation Service for Federated Learning: An Efficient, Secure, and More Resilient Realization

Efficient privacy-preserving aggregation for demand side management of residential loads

Verifiable Federated Learning With Privacy-Preserving Data Aggregation for Consumer Electronics

CORK: A privacy-preserving and lossless federated learning scheme for deep neural network

Blockchain-Based Efficiently Privacy-Preserving Federated Learning Framework Using Shamir Secret Sharing

GoCrowd: Obliviously Aggregating Crowd Wisdom With Quality Awareness in Crowdsourcing

Graph Sparsification-Based Secure Federated Learning for Consumer-Driven Internet of Things

Secure Single-Server Aggregation with (Poly)Logarithmic Overhead

基于相似度聚类的可信联邦安全聚合算法

On the Security of Verifiable and Oblivious Secure Aggregation for Privacy-Preserving Federated Learning

Malicious-Resistant Non-Interactive Verifiable Aggregation for Federated Learning

MASKCRYPT: Federated Learning with Selective Homomorphic Encryption

Information Theoretic Secure Aggregation With User Dropouts

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