On the public citation graph

FeDDkw – Federated Learning with Dynamic Kullback–Leibler-divergence Weight

https://doi.org/10.1145/3594779
0 signed · 40 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 See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.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/10.1038/s42256-020-0186-1openalex-snapshot
Secure, privacy-preserving and federated machine learning in medical imaging
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/mis.2020.2988525openalex-snapshot
A Secure Federated Transfer Learning Framework
inferredcites →https://doi.org/10.1109/msp.2020.2975749openalex-snapshot
Federated Learning: Challenges, Methods, and Future Directions
inferredcites →https://doi.org/10.1109/tccn.2021.3084406openalex-snapshot
Fast-Convergent Federated Learning With Adaptive Weighting
inferredcites →https://doi.org/10.1109/tii.2019.2942190openalex-snapshot
Blockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoT
inferredcites →https://doi.org/10.1109/tii.2019.2945367openalex-snapshot
Efficient and Privacy-Enhanced Federated Learning for Industrial Artificial Intelligence
inferredcites →https://doi.org/10.1109/tii.2020.3023430openalex-snapshot
DeepFed: Federated Deep Learning for Intrusion Detection in Industrial Cyber–Physical Systems
inferredcites →https://doi.org/10.1109/tii.2021.3064351openalex-snapshot
Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT
inferredcites →https://doi.org/10.1109/tii.2021.3075706openalex-snapshot
TrustFed: A Framework for Fair and Trustworthy Cross-Device Federated Learning in IIoT
inferredcites →https://doi.org/10.1109/tii.2021.3088057openalex-snapshot
Federated Transfer Learning Based Cross-Domain Prediction for Smart Manufacturing
inferredcites →https://doi.org/10.1109/tii.2021.3114513openalex-snapshot
Privacy-Preserving Optimal Energy Management for Smart Grid With Cloud-Edge Computing
inferredcites →https://doi.org/10.1109/tii.2021.3119038openalex-snapshot
Federated Learning for Cybersecurity: Concepts, Challenges, and Future Directions
inferredcites →https://doi.org/10.1109/tkde.2021.3124599openalex-snapshot
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
inferredcites →https://doi.org/10.1145/3372224openalex-snapshot
Proceedings of the 26th Annual International Conference on Mobile Computing and Networking
inferredcites →https://doi.org/10.1561/2200000083openalex-snapshot
Advances and Open Problems in Federated Learning
inferredcites →https://doi.org/10.1609/aaai.v34i04.5895openalex-snapshot
Practical Federated Gradient Boosting Decision Trees
inferredcites →https://doi.org/10.48550/arxiv.1602.05629openalex-snapshot
Communication-Efficient Learning of Deep Networks from Decentralized Data
inferredcites →https://doi.org/10.48550/arxiv.1902.01046openalex-snapshot
Towards Federated Learning at Scale: System Design
inferredcites →https://doi.org/10.48550/arxiv.1902.04885openalex-snapshot
Federated Machine Learning: Concept and Applications
inferredcites →https://doi.org/10.48550/arxiv.2007.07481openalex-snapshot
Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization
inferredcites →https://doi.org/10.48550/arxiv.2007.14513openalex-snapshot
Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge
inferredcites →https://doi.org/10.48550/arxiv.2010.10154openalex-snapshot
Federated Bayesian Optimization via Thompson Sampling

What points at this work

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

inferred← citeshttps://doi.org/10.2139/ssrn.4821752openalex-extracted
A New Methodology for Edge Intelligence Data Quality Evaluation in Idd and Non-Iid Dataset in Federated Learning
inferred← citeshttps://doi.org/10.1016/j.eswa.2025.130487openalex-snapshot
FedLBW: A loss-based weighting strategy for federated learning on non-IID data in wireless networks
inferred← citeshttps://doi.org/10.1016/j.future.2025.108177openalex-snapshot
FedTVD: balancing data quality and quantity for robust federated learning
inferred← citeshttps://doi.org/10.1016/j.iot.2025.101782openalex-snapshot
Privacy-preserving energy analytics in smart offices via container-based Federated Learning
inferred← citeshttps://doi.org/10.1016/j.neuri.2024.100182openalex-snapshot
KL-FedDis: A federated learning approach with distribution information sharing using Kullback-Leibler divergence for non-IID data
inferred← citeshttps://doi.org/10.1038/s41598-024-64122-4openalex-snapshot
Finding influential nodes in complex networks based on Kullback–Leibler model within the neighborhood
inferred← citeshttps://doi.org/10.1109/access.2024.3413069openalex-snapshot
A Multifaceted Survey on Federated Learning: Fundamentals, Paradigm Shifts, Practical Issues, Recent Developments, Partnerships, Trade-Offs, Trustworthiness, and Ways Forward
inferred← citeshttps://doi.org/10.1109/access.2025.3578301openalex-snapshot
Adding Data Quality to Federated Learning Performance Improvement
inferred← citeshttps://doi.org/10.1109/cai59869.2024.00185openalex-snapshot
On the Impact of Data Heterogeneity in Federated Learning Environments with Application to Healthcare Networks
inferred← citeshttps://doi.org/10.1109/iros58592.2024.10802073openalex-snapshot
FedRC: A Rapid-Converged Hierarchical Federated Learning Framework in Street Scene Semantic Understanding
inferred← citeshttps://doi.org/10.1109/jiot.2025.3602671openalex-snapshot
Detecting Poisoning Attacks in Quantized Federated Learning for IoT: A Trustworthy AI Approach
inferred← citeshttps://doi.org/10.1109/jiot.2026.3697298openalex-snapshot
Personalizing Federated Learning for Hierarchical Edge Networks With Non-IID Data
inferred← citeshttps://doi.org/10.1109/ojcs.2025.3567386openalex-snapshot
FOCC: A Synthetically Balanced Federated One-Class-Classification for Cyber Threat Intelligence in Software Defined Networking
inferred← citeshttps://doi.org/10.1109/tccn.2025.3569555openalex-snapshot
Personalized Model Partitioning and Aggregation for Federated Human Activity Recognition
inferred← citeshttps://doi.org/10.1109/tencon66050.2025.11375416openalex-snapshot
Data Oriented Fairness in Cross-Silo Federated Learning
inferred← citeshttps://doi.org/10.1109/tits.2025.3543235openalex-snapshot
Fast-Convergent and Communication-Alleviated Heterogeneous Hierarchical Federated Learning in Autonomous Driving
inferred← citeshttps://doi.org/10.1109/tnsm.2025.3576599openalex-snapshot
BDAFL: A Blockchain-Integrated Decentralized Asynchronous Federated Learning Algorithm in Industrial Internet
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.