On the public citation graph

Flex: Flexible Federated Learning Framework

https://doi.org/10.2139/ssrn.4764504
0 signed · 22 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 See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.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/10.1002/int.22582openalex-extracted
Improved autoencoder for unsupervised anomaly detection
inferredcites →https://doi.org/10.1007/978-3-030-58951-6_24openalex-extracted
Data Poisoning Attacks Against Federated Learning Systems
inferredcites →https://doi.org/10.1007/s40745-021-00362-9openalex-extracted
A Comprehensive Survey of Anomaly Detection Algorithms
inferredcites →https://doi.org/10.1016/j.compind.2021.103498openalex-extracted
A survey on anomaly detection for technical systems using LSTM networks
inferredcites →https://doi.org/10.1016/j.inffus.2022.09.011openalex-extracted
Survey on federated learning threats: Concepts, taxonomy on attacks and defences, experimental study and challenges
inferredcites →https://doi.org/10.1016/j.inffus.2023.101896openalex-extracted
Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation
inferredcites →https://doi.org/10.1093/bioinformatics/btac065openalex-extracted
Federated Random Forests can improve local performance of predictive models for various healthcare applications
inferredcites →https://doi.org/10.1109/jas.2024.124215openalex-extracted
A Tutorial on Federated Learning from Theory to Practice: Foundations, Software Frameworks, Exemplary Use Cases, and Selected Trends
inferredcites →https://doi.org/10.1109/jiot.2020.2977383openalex-extracted
Decentralized Privacy Using Blockchain-Enabled Federated Learning in Fog Computing
inferredcites →https://doi.org/10.1109/mis.2020.2988525openalex-extracted
A Secure Federated Transfer Learning Framework
inferredcites →https://doi.org/10.1109/tpds.2021.3056773openalex-extracted
Proof of Federated Learning: A Novel Energy-Recycling Consensus Algorithm
inferredcites →https://doi.org/10.1145/3338501.3357370openalex-extracted
A Hybrid Approach to Privacy-Preserving Federated Learning
inferredcites →https://doi.org/10.1145/3439950openalex-extracted
Deep Learning for Anomaly Detection
inferredcites →https://doi.org/10.1145/3555803openalex-extracted
Trustworthy AI: From Principles to Practices
inferredcites →https://doi.org/10.1609/aaai.v34i04.5895openalex-extracted
Practical Federated Gradient Boosting Decision Trees
inferredcites →https://doi.org/10.3115/v1/d14-1179openalex-extracted
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
inferredcites →https://doi.org/10.4230/tgdk.3.3.5openalex-extracted
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
inferredcites →https://doi.org/10.48550/arxiv.1807.00459openalex-extracted
How To Backdoor Federated Learning
inferredcites →https://doi.org/10.48550/arxiv.1812.01097openalex-extracted
LEAF: A Benchmark for Federated Settings
inferredcites →https://doi.org/10.48550/arxiv.1812.03224openalex-extracted
A Hybrid Approach to Privacy-Preserving Federated Learning
inferredcites →https://doi.org/10.48550/arxiv.2103.04628openalex-extracted
Personalized Federated Learning using Hypernetworks
inferredcites →https://doi.org/10.48550/arxiv.2107.11621openalex-extracted
FedLab: A Flexible Federated Learning Framework
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.