Paper Citation Record · LEDGER

Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning

As of 11 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2501.08002.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.08002 v2

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

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measured 45 of 45 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

45 of 45 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b340b827-c8b6-403b-8cdf-2102eb23c963 · outbound

This paper cites GPT-4, AGI, and the hunt for superintelligence,.

Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning GPT-4, AGI, and the hunt for superintelligence,

Reference 1

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Observation 3a336884-c525-436a-bbca-0efd7d568026 · outbound

This paper cites Artificial intelligence act: MEPs adopt landmark law,.

Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning Artificial intelligence act: MEPs adopt landmark law,

Reference 2

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Observation ac84299a-4d44-49ea-b542-6404761403b1 · outbound

This paper cites A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion,.

Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:35:28.028464Z

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Observation 579c07bb-1272-43b7-99a5-e760e8792a9e · outbound

This paper cites Towards calibrated and scalable uncertainty representations for neural networks,.

Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning Towards calibrated and scalable uncertainty representations for neural networks,

Reference 4

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Observation 8affb74b-26ce-4950-8d06-6ff1bebdc70a · outbound

This paper cites Understanding measures of uncertainty for adversarial example detection,.

Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning Understanding measures of uncertainty for adversarial example detection,

Reference 5

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Observation 978e3a22-e6a4-4aa0-a7d8-9c618fa2fdc7 · outbound

This paper cites The need for uncertainty quantification in machine-assisted medical decision making,.

Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning The need for uncertainty quantification in machine-assisted medical decision making,

Reference 6

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Source-reported events for the cited work

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Observation b3254858-e69e-43c3-a027-e3add9d96165 · outbound

This paper cites Federated learning in mobile edge networks: A comprehensive survey,.

Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning Federated learning in mobile edge networks: A comprehensive survey,

Reference 7

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Observation aa42e9e7-2536-4cdc-b546-a748c367d3fd · outbound

This paper cites Towards personalized federated learning,.

Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning Towards personalized federated learning,

Reference 8

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Observation e9849965-c0a1-435b-9700-2cbc08718f1f · outbound

This paper cites An aggregation-free federated learning for tackling data heterogeneity,.

Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning An aggregation-free federated learning for tackling data heterogeneity,

Reference 9

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Observation f3b64a47-6476-4e6b-a6c8-6b3e79d76703 · outbound

This paper cites FedHealth: A federated transfer learning framework for wearable healthcare,.

Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning FedHealth: A federated transfer learning framework for wearable healthcare,

Reference 10

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Observation d8d7bfb0-14f1-423e-8ad0-0462158052fa · outbound

This paper cites Wireless distributed learning: A new hybrid split and federated learning approach,.

Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning Wireless distributed learning: A new hybrid split and federated learning approach,

Reference 11

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Source-reported events for the cited work

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Observation cf29075c-253d-49f5-98dc-2c868cb083fc · outbound

This paper cites Communication-efficient federated learning and permissioned blockchain for digital twin edge networks,.

Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning Communication-efficient federated learning and permissioned blockchain for digital twin edge networks,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:35:27.914158Z

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Observation fbc56034-012d-475f-9be8-67b68ef42307 · outbound

This paper cites Wild patterns: Ten years after the rise of adversarial machine learning,.

Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning Wild patterns: Ten years after the rise of adversarial machine learning,

Reference 13

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Observation 89f352b6-f062-4e99-8a30-5715714c0077 · outbound

This paper cites Investigation of deep learning architectures and features for adversarial machine learning attacks in modulation classifications,.

Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning Investigation of deep learning architectures and features for adversarial machine learning attacks in modulation classifications,

Reference 14

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Observation 07e58636-9eca-4931-b78b-6f2cb8f6346a · outbound

This paper cites Evidential classification for defending against adversarial examples in radio signal classification,.

Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning Evidential classification for defending against adversarial examples in radio signal classification,

Reference 15

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Observation e6dc298d-8841-46cb-925a-ea96f09da71d · outbound

This paper cites Bayesian optimisation-driven adversarial poisoning attacks against distributed learning,.

Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning Bayesian optimisation-driven adversarial poisoning attacks against distributed learning,

Reference 16

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Observation 8482b91c-65af-4ca9-bca8-fdd8573714c2 · outbound

This paper cites Local model poisoning attacks to Byzantine-robust federated learning,.

Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning Local model poisoning attacks to Byzantine-robust federated learning,

Reference 17

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Observation 1b0f2636-6509-4fdb-9b6b-bb8aa8ef137a · outbound

This paper cites Membership inference attacks against machine learning models,.

Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning Membership inference attacks against machine learning models,

Reference 18

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Observation 091de492-09a4-4d77-9c2a-31a74842d413 · outbound

This paper cites TrustFed: A framework for fair and trustworthy cross-device federated learning in IIoT,.

Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning TrustFed: A framework for fair and trustworthy cross-device federated learning in IIoT,

Reference 19

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Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning Distributed intelligence in wireless networks,

Reference 20

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Observation 90e55c6d-1b70-44ee-a63f-5aa9c85c46ae · outbound

This paper cites MPAF: Model poisoning attacks to federated learning based on fake clients,.

Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning MPAF: Model poisoning attacks to federated learning based on fake clients,

Reference 21

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Observation 5861a2d7-c2e9-491c-bfa2-db717e0b8caf · outbound

This paper cites A little is enough: Circumvent- ing defenses for distributed learning,.

Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning A little is enough: Circumvent- ing defenses for distributed learning,

Reference 22

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This paper cites Data poisoning attacks on federated learning by using adversarial samples,.

Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning Data poisoning attacks on federated learning by using adversarial samples,

Reference 23

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Observation 4ee0b1a5-b510-4090-a466-3e3895313259 · outbound

This paper cites Data-agnostic model poisoning against federated learning: A graph autoencoder approach,.

Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning Data-agnostic model poisoning against federated learning: A graph autoencoder approach,

Reference 24

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Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning Hidden trigger backdoor attacks,

Reference 25

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Observation 8fb72856-5070-470e-a8d3-d0bbefa6ef2a · outbound

This paper cites How To Backdoor Federated Learning.

Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning How To Backdoor Federated Learning

Reference 26

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Observation 264bbaf5-fea6-4e54-a442-b2f8df9e2c01 · outbound

This paper cites Ditto: Fair and Robust Federated Learning Through Personalization.

Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning Ditto: Fair and Robust Federated Learning Through Personalization

Reference 27

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This paper cites Manipulating the byzantine: Opti- mizing model poisoning attacks and defenses for federated learning,.

Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning Manipulating the byzantine: Opti- mizing model poisoning attacks and defenses for federated learning,

Reference 28

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Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning Intriguing properties of neural networks,

Reference 29

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Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning Coun- termeasures against adversarial examples in radio signal classification,

Reference 30

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Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning Learning Differentially Private Recurrent Language Models

Reference 31

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This paper cites Machine learning with adversaries: Byzantine tolerant gradient descent,.

Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning Machine learning with adversaries: Byzantine tolerant gradient descent,

Reference 32

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Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning A subspace, interior, and con- jugate gradient method for large-scale bound-constrained minimization problems,

Reference 33

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Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning An interior trust region approach for nonlinear minimization subject to bounds,

Reference 34

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Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning Efficient global optimization of expensive black-box functions,

Reference 35

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Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning Practical Bayesian Optimization of Machine Learning Algorithms

Reference 36

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Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning A Tutorial on Bayesian Optimization

Reference 37

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Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning Unresolved cited work

Reference 38

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Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning Scalable Bayesian Optimization Using Deep Neural Networks

Reference 39

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Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning Lifelong Bayesian Optimization

Reference 40

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Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning The reparameterization trick for acquisition functions

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Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning Auto-Encoding Variational Bayes

Reference 42

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Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and Benchmark

Reference 43

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Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning ImageNet classification with deep convolutional neural networks,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:35:27.622232Z

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Observation bdfbfb4f-4967-4182-80a0-403c1c568554 · outbound

This paper cites His research interests include decentralized computing, federated learning, privacy- preserving AI and Blockchain.

Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning His research interests include decentralized computing, federated learning, privacy- preserving AI and Blockchain

Reference 2005

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:35:27.608387Z

Source-reported events for the cited work

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Pith citing papers

No inbound Pith citation observations are available.