Paper Citation Record · LEDGER

Robust Federated Learning against Model Perturbation in Edge Networks

As of 8 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2505.24728.

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

pith.paper-citation-record.v1
2505.24728 v1

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:21:13.264665Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

18 of 18 outbound references displayed

verified exact0
verified fuzzy18
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 60d1312a-d93f-498e-8ac8-d894c2710cd9 · outbound

This paper cites Time-sensitive learning for heterogeneous federated edge intelligence,.

Robust Federated Learning against Model Perturbation in Edge Networks Time-sensitive learning for heterogeneous federated edge intelligence,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:21:16.075992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f4cd8caa-d276-4ad1-8a20-4dca83d32748 · outbound

This paper cites Distributed traffic synthesis and classification in edge networks: A federated self-supervised learning approach,.

Robust Federated Learning against Model Perturbation in Edge Networks Distributed traffic synthesis and classification in edge networks: A federated self-supervised learning approach,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:21:15.937917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1e08b5c2-9ccb-44d6-8a8d-4257a271a766 · outbound

This paper cites Federated generative learning for digital twin network modeling,.

Robust Federated Learning against Model Perturbation in Edge Networks Federated generative learning for digital twin network modeling,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:21:15.775960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d954e877-f6b0-40b2-9518-fc168be905b2 · outbound

This paper cites Robust design of federated learning for edge- intelligent networks,.

Robust Federated Learning against Model Perturbation in Edge Networks Robust design of federated learning for edge- intelligent networks,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:21:15.665337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1638c298-7fb5-45a3-8cfa-d1f508a13103 · outbound

This paper cites Federated learning with sparsified model perturbation: Improving accuracy under client-level differential privacy,.

Robust Federated Learning against Model Perturbation in Edge Networks Federated learning with sparsified model perturbation: Improving accuracy under client-level differential privacy,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:21:15.482084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 48c07256-7720-4758-bf36-3c954dba4465 · outbound

This paper cites Byzantine-resilient secure federated learning,.

Robust Federated Learning against Model Perturbation in Edge Networks Byzantine-resilient secure federated learning,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:21:15.303711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5be6db7b-b5fc-47ce-b148-e5bcdacbaf3b · outbound

This paper cites Fl- wbc: Enhancing robustness against model poisoning attacks in federated learning from a client perspective,.

Robust Federated Learning against Model Perturbation in Edge Networks Fl- wbc: Enhancing robustness against model poisoning attacks in federated learning from a client perspective,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:21:15.141213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d6c552ef-02f9-4c36-aab7-aeb216691991 · outbound

This paper cites Fedinv: Byzantine-robust federated learning by inversing local model updates,.

Robust Federated Learning against Model Perturbation in Edge Networks Fedinv: Byzantine-robust federated learning by inversing local model updates,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:21:15.003072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2f2b6f61-bd60-407b-b4fa-418ccd3ae1d1 · outbound

This paper cites DAdaQuant: Doubly-adaptive quantization for communication-efficient federated learning,.

Robust Federated Learning against Model Perturbation in Edge Networks DAdaQuant: Doubly-adaptive quantization for communication-efficient federated learning,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:21:14.898115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8ab38cf5-c8b7-40ca-8f68-62c95e3db742 · outbound

This paper cites Federated learning over noisy channels: Conver- gence analysis and design examples,.

Robust Federated Learning against Model Perturbation in Edge Networks Federated learning over noisy channels: Conver- gence analysis and design examples,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:21:14.766063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9a097e80-2453-4de3-be36-b871d0a3ae5f · outbound

This paper cites Sharpness-aware minimization for efficiently improving generalization,.

Robust Federated Learning against Model Perturbation in Edge Networks Sharpness-aware minimization for efficiently improving generalization,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:21:14.609736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bfeee286-4ed4-45fa-b318-28fee3cbfc29 · outbound

This paper cites Convergence of federated learning over a noisy downlink,.

Robust Federated Learning against Model Perturbation in Edge Networks Convergence of federated learning over a noisy downlink,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:21:14.421929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fdb4b690-1d50-49ed-b5a2-a7fb3f0704ee · outbound

This paper cites Uveqfed: Universal vector quantization for federated learning,.

Robust Federated Learning against Model Perturbation in Edge Networks Uveqfed: Universal vector quantization for federated learning,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:21:14.257392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 21efcc8a-43ff-4898-a8f9-2be2d9a97d17 · outbound

This paper cites SCAFFOLD: Stochastic controlled averaging for federated learning,.

Robust Federated Learning against Model Perturbation in Edge Networks SCAFFOLD: Stochastic controlled averaging for federated learning,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:21:14.101351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 20b19aec-801b-4610-a005-84a1126a7054 · outbound

This paper cites Generalized feder- ated learning via sharpness aware minimization,.

Robust Federated Learning against Model Perturbation in Edge Networks Generalized feder- ated learning via sharpness aware minimization,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:21:13.923459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f356ea37-296f-46eb-95b1-8a653d3aca90 · outbound

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

Robust Federated Learning against Model Perturbation in Edge Networks Communication-efficient learning of deep networks from decentralized data,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:21:13.738536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e6247c79-2e30-4a05-bb30-a80f6aa276cb · outbound

This paper cites Federated learning based on dynamic regularization,.

Robust Federated Learning against Model Perturbation in Edge Networks Federated learning based on dynamic regularization,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:21:13.573025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 043ca910-77de-4cb2-831c-0fc498a52763 · outbound

This paper cites Visualizing the loss landscape of neural nets,.

Robust Federated Learning against Model Perturbation in Edge Networks Visualizing the loss landscape of neural nets,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:21:13.435826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

No inbound Pith citation observations are available.