Paper Citation Record · LEDGER

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks

As of 6 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2508.18737.

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

pith.paper-citation-record.v1
2508.18737 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T16:20:23.398486Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

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

48 of 48 outbound references displayed

verified exact1
verified fuzzy34
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 381d9cb3-a51c-49c2-abec-9532463295d8 · outbound

This paper cites Communication-efficient learning of deep networks from decentralized data,.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Communication-efficient learning of deep networks from decentralized data,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-05T16:20:23.229261Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a8313c4-2ec0-4bf9-b022-480e2384d4d4 · outbound

This paper cites Federated machine learning: Concept and applications,.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Federated machine learning: Concept and applications,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:20:23.914839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a04a6390-60bb-40dd-8bcc-d404cf2db3cc · outbound

This paper cites The eu general data protection regulation (gdpr): Eu- ropean regulation that has a global impact,.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks The eu general data protection regulation (gdpr): Eu- ropean regulation that has a global impact,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:20:23.900489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a06057ec-871d-44a6-a396-ef3736bff5c1 · outbound

This paper cites Ma- chine learning with adversaries: Byzantine tolerant gradient descent,.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Ma- chine learning with adversaries: Byzantine tolerant gradient descent,

Reference 4

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Source-reported events for the cited work

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Observation e0e50ca1-ae20-4525-9ce1-8881a736a465 · outbound

This paper cites Can machine learning be secure?.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Can machine learning be secure?

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:20:23.880046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f5389eb4-e418-43c8-a276-e0f2801d487f · outbound

This paper cites How to backdoor federated learning,.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks How to backdoor federated learning,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:20:23.868054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c474600c-7d07-49f6-b4f9-71debefa8883 · outbound

This paper cites A comprehensive survey on poisoning attacks and countermeasures in machine learning,.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks A comprehensive survey on poisoning attacks and countermeasures in machine learning,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:20:23.857057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e7693f2b-cb57-4db3-8947-addb45ffa6d6 · outbound

This paper cites Poisoning attacks in federated learning: A survey,.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Poisoning attacks in federated learning: A survey,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:20:23.846464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 47d1746e-ef28-4dea-8ba8-05c549496350 · outbound

This paper cites Local model poisoning attacks to {Byzantine-Robust} federated learning,.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Local model poisoning attacks to {Byzantine-Robust} federated learning,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T16:20:23.259036Z

Source-reported events for the cited work

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Observation 599e9319-3925-410d-92a2-62243fb96428 · outbound

This paper cites On the byzantine robustness of clustered federated learning,.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks On the byzantine robustness of clustered federated learning,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:20:23.830514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 43e6b741-960f-4d19-be1e-96de679bb75d · outbound

This paper cites A symbolic representation of time series, with implications for streaming algorithms,.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks A symbolic representation of time series, with implications for streaming algorithms,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:20:23.820127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5e9db837-0d2b-4ecb-98a7-3cfee1fe8ecc · outbound

This paper cites Chapter 11 - recommendation engines,.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Chapter 11 - recommendation engines,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:20:23.809183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f313b629-9853-4a0e-b419-37c32c4d5d2a · outbound

This paper cites an unresolved cited work.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-08-05T16:20:23.797909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 87d73c1c-515a-4afd-993e-cd3a9d7b6e0d · outbound

This paper cites Fedrdf: A robust and dynamic aggregation function against poisoning attacks in federated learning,.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Fedrdf: A robust and dynamic aggregation function against poisoning attacks in federated learning,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:20:23.788033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 7b5ef8d4-baa1-4ce1-a1c5-7b73bdf0cf26 · outbound

This paper cites Threats to Federated Learning: A Survey.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Threats to Federated Learning: A Survey

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T16:20:23.280461Z

Source-reported events for the cited work

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Observation 64142875-b930-4b86-b09e-e68e2945c6d3 · outbound

This paper cites Wavelet transform application for/in non-stationary time-series analysis: A re- view,.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Wavelet transform application for/in non-stationary time-series analysis: A re- view,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:20:23.778098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 63bef37c-27ba-445b-80d5-ce5a43784291 · outbound

This paper cites Discrete wavelet transform-based time series analysis and mining,.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Discrete wavelet transform-based time series analysis and mining,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:20:23.766564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a93b9fb8-c63a-40be-a418-712d02c0c669 · outbound

This paper cites Beats: Blocks of eigenvalues algorithm for time series segmentation,.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Beats: Blocks of eigenvalues algorithm for time series segmentation,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:20:23.756069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 26dd89fb-107e-46de-b749-3d8cda4c060c · outbound

This paper cites Multibeats: Blocks of eigenvalues algorithm for multivariate time series dimension- ality reduction,.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Multibeats: Blocks of eigenvalues algorithm for multivariate time series dimension- ality reduction,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:20:23.745999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c774bb3f-b250-4290-9697-f47bd441b0d1 · outbound

This paper cites Dimensionality reduction for fast similarity search in large time series databases,.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Dimensionality reduction for fast similarity search in large time series databases,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:20:23.735381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation bd19dd00-c828-40ef-b1ab-c1f25f5065b5 · outbound

This paper cites Least squares quantization in pcm,.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Least squares quantization in pcm,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:20:23.725299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation db17e440-12df-414d-b8da-d4e20d53f9d8 · outbound

This paper cites Gaussian mixture models,.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Gaussian mixture models,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:20:23.713742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 4a9e022f-a975-4af0-9e80-d91dcddae1b6 · outbound

This paper cites Spectral clustering,.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Spectral clustering,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:20:23.703971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 42960f1a-c0c9-4de1-9f9f-951717d56db0 · outbound

This paper cites A tutorial on spectral clustering,.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks A tutorial on spectral clustering,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:20:23.693674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2955c048-6ed6-41f2-873b-aa48855b6e93 · outbound

This paper cites Learning spectral clustering,.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Learning spectral clustering,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:20:23.683539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1231c7e1-66fb-4069-a8c1-e02f4f90065f · outbound

This paper cites an unresolved cited work.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-08-05T16:20:23.673386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8f3e2de0-af02-4fdf-88df-8645bf7cdc79 · outbound

This paper cites Byzantine-robust dis- tributed learning: Towards optimal statistical rates,.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Byzantine-robust dis- tributed learning: Towards optimal statistical rates,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:20:23.663616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6b0bb8f5-f2eb-49f5-bd66-87ce00e78300 · outbound

This paper cites Mitigating Sybils in Federated Learning Poisoning.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Mitigating Sybils in Federated Learning Poisoning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-05T16:20:23.326364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bbf413fc-ed2e-4818-8584-667e88063656 · outbound

This paper cites FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-05T16:20:23.330213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4bdafc3c-fe5c-4bc3-9770-019cd40190f2 · outbound

This paper cites Learning to Detect Malicious Clients for Robust Federated Learning.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Learning to Detect Malicious Clients for Robust Federated Learning

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-05T16:20:23.334034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32260704-5f17-494c-a07d-080789b5bf11 · outbound

This paper cites Fldetector: Defending federated learning against model poisoning attacks via detecting ma- licious clients,.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Fldetector: Defending federated learning against model poisoning attacks via detecting ma- licious clients,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:20:23.653677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1468ceec-54b1-4361-b304-a23d3f116390 · outbound

This paper cites Sentinel: An Aggregation Function to Secure Decentralized Federated Learning.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Sentinel: An Aggregation Function to Secure Decentralized Federated Learning

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-05T16:20:23.341029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7fda1ab3-96ea-4943-ab5a-3a8a38bc3dff · outbound

This paper cites Lomar: A local defense against poisoning attack on federated learning,.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Lomar: A local defense against poisoning attack on federated learning,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:20:23.643991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2a4c808a-a298-4864-a3d0-3602aa68a380 · outbound

This paper cites Feddmc: Efficient and robust federated learning via detecting malicious clients,.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Feddmc: Efficient and robust federated learning via detecting malicious clients,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:20:23.633585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1e00fb4c-97fe-48c3-ad79-66d8edfac3e8 · outbound

This paper cites Byzantine-robust federated learning through collaborative malicious gradient filtering,.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Byzantine-robust federated learning through collaborative malicious gradient filtering,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:20:23.622099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 08270032-b270-469e-9116-774d70adf632 · outbound

This paper cites Shielding Federated Learning: Robust Aggregation with Adaptive Client Selection.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Shielding Federated Learning: Robust Aggregation with Adaptive Client Selection

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-08-05T16:20:23.464015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1e13b958-5973-499b-bb5f-69487489e97d · outbound

This paper cites The sybil attack,.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks The sybil attack,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:20:23.611088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b0345689-8b3b-4463-a97e-93abe1a1d322 · outbound

This paper cites Fully decentralized federated learning,.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Fully decentralized federated learning,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:20:23.599994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0008192c-8c78-465d-993c-59221b284f48 · outbound

This paper cites Back to the drawing board: A critical evaluation of poisoning attacks on production federated learning,.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Back to the drawing board: A critical evaluation of poisoning attacks on production federated learning,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:20:23.588165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e3283ec4-26ce-46ef-a250-138694173d90 · outbound

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

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks A little is enough: Circumvent- ing defenses for distributed learning,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:20:23.575820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1881814b-3e7c-403f-b10f-579b6d34383b · outbound

This paper cites Flower: A Friendly Federated Learning Research Framework.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Flower: A Friendly Federated Learning Research Framework

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-05T16:20:23.372278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 808826a2-3f88-4789-b1f1-d1a1b2008698 · outbound

This paper cites Emnist: Extending mnist to handwritten letters,.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Emnist: Extending mnist to handwritten letters,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:20:23.564285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 4cbee77e-8b8c-4a68-b685-1ab839bde23c · outbound

This paper cites LEAF: A Benchmark for Federated Settings.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks LEAF: A Benchmark for Federated Settings

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-05T16:20:23.380270Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1abdb28c-441c-4ffc-84f8-7bbe8db205fa · outbound

This paper cites Entropy estimates of small data sets,.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Entropy estimates of small data sets,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:20:23.554368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation bf324a23-5d54-469d-ad5b-a8c8b1690282 · outbound

This paper cites Sageflow: Robust federated learning against both stragglers and adversaries,.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Sageflow: Robust federated learning against both stragglers and adversaries,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:20:23.544099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9ab789eb-755b-4c16-95ed-704b4bbe9b2b · outbound

This paper cites Trojdrl: evaluation of back- door attacks on deep reinforcement learning,.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Trojdrl: evaluation of back- door attacks on deep reinforcement learning,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:20:23.534060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation cbcaf4ec-d996-48ae-89ca-58a22d56fa55 · outbound

This paper cites Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-05T16:20:23.394900Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4cf9adc4-b4d3-4643-bfe8-19b8bcc4ff0e · outbound

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

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Manipulating the byzantine: Opti- mizing model poisoning attacks and defenses for federated learning,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:20:23.523759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Pith citing papers

No inbound Pith citation observations are available.