On the public citation graph

Consideration of FedProx in Privacy Protection

https://doi.org/10.3390/electronics12204364

0 signed · 52 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/11681878_14openalex-snapshot

Calibrating Noise to Sensitivity in Private Data Analysis

inferredcites →https://doi.org/10.1007/11787006_1openalex-snapshot

Differential Privacy

inferredcites →https://doi.org/10.1007/978-3-030-59410-7_33openalex-snapshot

FedSel: Federated SGD Under Local Differential Privacy with Top-k Dimension Selection

inferredcites →https://doi.org/10.1007/978-3-319-59870-3_14openalex-snapshot

Computing Aggregates Over Numeric Data with Personalized Local Differential Privacy

inferredcites →https://doi.org/10.1007/s11704-020-0103-0openalex-snapshot

Mean estimation over numeric data with personalized local differential privacy

inferredcites →https://doi.org/10.1109/bigcom57025.2022.00018openalex-snapshot

Federated Learning With Personalized Differential Privacy Combining Client Selection

inferredcites →https://doi.org/10.1109/csf.2017.11openalex-snapshot

Rényi Differential Privacy

inferredcites →https://doi.org/10.1109/icc42927.2021.9500632openalex-snapshot

COFEL: Communication-Efficient and Optimized Federated Learning with Local Differential Privacy

inferredcites →https://doi.org/10.1109/icccs52626.2021.9449232openalex-snapshot

Federated Learning with Personalized Local Differential Privacy

inferredcites →https://doi.org/10.1109/icde.2015.7113353openalex-snapshot

Conservative or liberal? Personalized differential privacy

inferredcites →https://doi.org/10.1109/icde.2016.7498248openalex-snapshot

Private spatial data aggregation in the local setting

inferredcites →https://doi.org/10.1109/infocom42981.2021.9488825openalex-snapshot

AdaPDP: Adaptive Personalized Differential Privacy

inferredcites →https://doi.org/10.1109/jiot.2020.2991416openalex-snapshot

Personalized Federated Learning With Differential Privacy

inferredcites →https://doi.org/10.1109/jiot.2020.3037194openalex-snapshot

Local Differential Privacy-Based Federated Learning for Internet of Things

inferredcites →https://doi.org/10.1109/msp.2012.2211477openalex-snapshot

The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web]

inferredcites →https://doi.org/10.1109/tdsc.2020.3005909openalex-snapshot

Privacy-Preserving Federated Deep Learning with Irregular Users

inferredcites →https://doi.org/10.1109/tifs.2020.2988575openalex-snapshot

Federated Learning With Differential Privacy: Algorithms and Performance Analysis

inferredcites →https://doi.org/10.1109/tkde.2018.2841360openalex-snapshot

A Utility-Optimized Framework for Personalized Private Histogram Estimation

inferredcites →https://doi.org/10.1109/tpds.2013.18openalex-snapshot

Privacy Preserving Back-Propagation Neural Network Learning Made Practical with Cloud Computing

inferredcites →https://doi.org/10.1137/090756090openalex-snapshot

What Can We Learn Privately?

inferredcites →https://doi.org/10.1145/2976749.2978318openalex-snapshot

Deep Learning with Differential Privacy

inferredcites →https://doi.org/10.1145/3133956.3133982openalex-snapshot

Practical Secure Aggregation for Privacy-Preserving Machine Learning

inferredcites →https://doi.org/10.1145/3338501.3357370openalex-snapshot

A Hybrid Approach to Privacy-Preserving Federated Learning

inferredcites →https://doi.org/10.1145/3378679.3394533openalex-snapshot

LDP-Fed

inferredcites →https://doi.org/10.1155/2021/3344862openalex-snapshot

Utility Optimization of Federated Learning with Differential Privacy

inferredcites →https://doi.org/10.1155/2021/6684179openalex-snapshot

PLDP: Personalized Local Differential Privacy for Multidimensional Data Aggregation

inferredcites →https://doi.org/10.1561/0400000042openalex-snapshot

The Algorithmic Foundations of Differential Privacy

inferredcites →https://doi.org/10.24963/ijcai.2021/202openalex-snapshot

Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimization

inferredcites →https://doi.org/10.24963/ijcai.2021/217openalex-snapshot

LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy

inferredcites →https://doi.org/10.3390/e25030485openalex-snapshot

PLDP-FL: Federated Learning with Personalized Local Differential Privacy

inferredcites →https://doi.org/10.5555/2946645.2946662openalex-snapshot

Extremal mechanisms for local differential privacy

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-032-10344-4_4openalex-snapshot

A Methodology and Tool for Automatic Workload Distribution. A Case Study on Federated Learning

inferred← citeshttps://doi.org/10.1007/978-3-032-30005-8_16openalex-snapshot

Advancing Disability Inclusion in Smart Urban Infrastructure Through Predictive Machine Learning Models: A Survey

inferred← citeshttps://doi.org/10.1007/978-981-97-7241-4_8openalex-snapshot

A Secure Dynamic Incentive Scheme for Federated Learning

inferred← citeshttps://doi.org/10.1007/s12083-026-02265-zopenalex-snapshot

A robust framework for secure cardiovascular risk prediction: An architectural case study of differentially private federated learning

inferred← citeshttps://doi.org/10.1007/s44443-026-00510-2openalex-snapshot

Review on federated optimization in multi-tier architectures

inferred← citeshttps://doi.org/10.1016/j.bspc.2026.111065openalex-snapshot

A unified framework for integrated prediction and personalized management of hormonal and mental health disorders

inferred← citeshttps://doi.org/10.1016/j.comnet.2026.112264openalex-snapshot

LDSG-MLP: Research on a lightweight deep learning DDoS intrusion detection method for SDN

inferred← citeshttps://doi.org/10.1016/j.future.2025.107783openalex-snapshot

Federated adaptive pruning with differential privacy

inferred← citeshttps://doi.org/10.1016/j.imu.2025.101691openalex-snapshot

FedDeepInsight—A privacy-first federated learning architecture for medical data

inferred← citeshttps://doi.org/10.1016/j.microc.2025.115412openalex-snapshot

Federated learning-enabled CNT sensor networks for VOC detection and environmental monitoring

inferred← citeshttps://doi.org/10.1088/1742-6596/3211/1/012003openalex-snapshot

FedProx-SA: A Privacy-Preserving Federated Learning Framework with Secure Aggregation for Bearing Fault Diagnosis

inferred← citeshttps://doi.org/10.1109/access.2025.3606865openalex-snapshot

Model-Oriented Orchestration for Federated Learning: Personalization Through Intelligent Client Matching

inferred← citeshttps://doi.org/10.1109/cbase64041.2024.10824396openalex-snapshot

Federated Dynamic Client Selection Based on Comprehensive Performance Evaluation

inferred← citeshttps://doi.org/10.1109/cisat66811.2025.11181802openalex-snapshot

Privacy-Enhanced Federated Learning for Distributed Heterogeneous Data

inferred← citeshttps://doi.org/10.1109/jiot.2025.3558560openalex-snapshot

Federated Learning Framework for LoRaWAN-Enabled IIoT Communication: A Case Study

inferred← citeshttps://doi.org/10.1109/tetci.2025.3575420openalex-snapshot

Enhancing Federated Learning Through Differential Privacy: Introducing FedHybrid for Multicenter Diverse Heart Disease Datasets

inferred← citeshttps://doi.org/10.1109/tqe.2025.3607689openalex-snapshot

DT-QFL: Dual-Timeline Quantum Federated Learning With Time-Symmetric Updates, Temporal Memory Kernels, and Reversed Gradient Dynamics

inferred← citeshttps://doi.org/10.1109/trustcom63139.2024.00091openalex-snapshot

Secure Federated Learning Schemes Based on Multi-Key Homomorphic Encryption

inferred← citeshttps://doi.org/10.2139/ssrn.5062070openalex-snapshot

FedDeepInsight - A Privacy-First Federated Learning Architecture for Medical Data

inferred← citeshttps://doi.org/10.2174/9798898810030125040005openalex-snapshot

Federated Learning for Enhanced Intrusion Detection: Combating Digital Deception in IoTEnabled Social Networks

inferred← citeshttps://doi.org/10.3390/math12162469openalex-snapshot

Graph Neural Network Based Asynchronous Federated Learning for Digital Twin-Driven Distributed Multi-Agent Dynamical Systems

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.