Cover Image

Compressed Private Aggregation for Scalable and Robust Federated Learning over Massive Networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Lang, Natalie, Shlezinger, Nir, D'Oliveira, Rafael G. L., Rouayheb, Salim El
Format:	Preprint
Published:	2023
Subjects:	Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Error Correction Capabilities of Non-Linear Cryptographic Hash Functions
by: Cohen, Alejandro, et al.
Published: (2024)

Optimal Computational Secret Sharing
by: Aureliano, Igor L., et al.
Published: (2025)

Random Walk Learning and the Pac-Man Attack
by: Chen, Xingran, et al.
Published: (2025)

Secure and Private Federated Learning: Achieving Adversarial Resilience through Robust Aggregation
by: Yang, Kun, et al.
Published: (2025)

Secure Stateful Aggregation: A Practical Protocol with Applications in Differentially-Private Federated Learning
by: Ball, Marshall, et al.
Published: (2024)

Fluent: Round-efficient Secure Aggregation for Private Federated Learning
by: Li, Xincheng, et al.
Published: (2024)

DP-BREM: Differentially-Private and Byzantine-Robust Federated Learning with Client Momentum
by: Gu, Xiaolan, et al.
Published: (2023)

FederBoost: Private Federated Learning for GBDT
by: Tian, Zhihua, et al.
Published: (2020)

Armadillo: Robust Single-Server Secure Aggregation for Federated Learning with Input Validation
by: Ma, Yiping, et al.
Published: (2025)

Samplable Anonymous Aggregation for Private Federated Data Analysis
by: Talwar, Kunal, et al.
Published: (2023)

On Using Secure Aggregation in Differentially Private Federated Learning with Multiple Local Steps
by: Heikkilä, Mikko A.
Published: (2024)

Efficient Byzantine-Robust Privacy-Preserving Federated Learning via Dimension Compression
by: Qin, Xian, et al.
Published: (2025)

Flamingo: Multi-Round Single-Server Secure Aggregation with Applications to Private Federated Learning
by: Ma, Yiping, et al.
Published: (2023)

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation
by: Xu, Jie, et al.
Published: (2026)

Private Aggregate Queries to Untrusted Databases
by: Hafiz, Syed Mahbub, et al.
Published: (2024)

EdgeDetect: Importance-Aware Gradient Compression with Homomorphic Aggregation for Federated Intrusion Detection
by: Mohammad, Noor Islam S.
Published: (2026)

PriRoAgg: Achieving Robust Model Aggregation with Minimum Privacy Leakage for Federated Learning
by: Hou, Sizai, et al.
Published: (2024)

An Efficient Hybrid Key Exchange Mechanism
by: Kim, Benjamin D., et al.
Published: (2025)

Cryptanalysis via Machine Learning Based Information Theoretic Metrics
by: Kim, Benjamin D., et al.
Published: (2025)

LiteChain: A Lightweight Blockchain for Verifiable and Scalable Federated Learning in Massive Edge Networks
by: Chen, Handi, et al.
Published: (2025)

Boosting Communication Efficiency of Federated Learning's Secure Aggregation
by: Nazemi, Niousha, et al.
Published: (2024)

MAUI: Reconstructing Private Client Data in Federated Transfer Learning
by: Dabholkar, Ahaan, et al.
Published: (2025)

Improving Parameter-Efficient Federated Learning with Differentially Private Refactorization
by: Tran, Linh, et al.
Published: (2026)

Private Count Release: A Simple and Scalable Approach for Private Data Analytics
by: Rogers, Ryan
Published: (2024)

Scalable Private Search with Wally
by: Asi, Hilal, et al.
Published: (2024)

Secure Aggregation in Federated Learning using Multiparty Homomorphic Encryption
by: Hosseini, Erfan, et al.
Published: (2025)

Efficient Secure Aggregation for Privacy-Preserving Federated Machine Learning
by: Behnia, Rouzbeh, et al.
Published: (2023)

Voltran: Unlocking Trust and Confidentiality in Decentralized Federated Learning Aggregation
by: Wang, Hao, et al.
Published: (2024)

Segmented Private Data Aggregation in the Multi-message Shuffle Model
by: Wang, Shaowei, et al.
Published: (2024)

Differentially Private Multi-objective Selection: Pareto and Aggregation Approaches
by: Farias, Victor A. E., et al.
Published: (2024)

Hybrid Reputation Aggregation: A Robust Defense Mechanism for Adversarial Federated Learning in 5G and Edge Network Environments
by: Sheikhi, Saeid, et al.
Published: (2025)

DDP-SA: Scalable Privacy-Preserving Federated Learning via Distributed Differential Privacy and Secure Aggregation
by: Wei, Wenjing, et al.
Published: (2026)

Scalable Differentially Private Sketches under Continual Observation
by: Holland, Rayne
Published: (2025)

A Critical Look into Threshold Homomorphic Encryption for Private Average Aggregation
by: Morona-Mínguez, Miguel, et al.
Published: (2026)

Securing Private Federated Learning in a Malicious Setting: A Scalable TEE-Based Approach with Client Auditing
by: Takagi, Shun, et al.
Published: (2025)

An Experimental Study of Machine Learning-Based Intrusion Detection for OPC UA over Industrial Private 5G Networks
by: Ha, Song Son, et al.
Published: (2026)

Harnessing Sparsification in Federated Learning: A Secure, Efficient, and Differentially Private Realization
by: Xu, Shuangqing, et al.
Published: (2025)

Efficient Full-Stack Private Federated Deep Learning with Post-Quantum Security
by: Zhang, Yiwei, et al.
Published: (2025)

Private and Communication-Efficient Federated Learning based on Differentially Private Sketches
by: Zhang, Meifan, et al.
Published: (2024)

When Secure Aggregation Falls Short: Achieving Long-Term Privacy in Asynchronous Federated Learning for LEO Satellite Networks
by: Elmahallawy, Mohamed, et al.
Published: (2025)