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Paper Citation Record · LEDGER

An Empirical Study of Vulnerability Detection using Federated Learning

As of 12 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2411.16099.

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

pith.paper-citation-record.v1
2411.16099 v1

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T13:37:19.234618Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

50 of 50 outbound references displayed

  • verified exact1
  • verified fuzzy42
  • unresolved7
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 28ed6b1d-d53d-45a0-8bb8-82f8b220401c · outbound

This paper cites Reef: A framework for collecting real- world vulnerabilities and fixes,.

An Empirical Study of Vulnerability Detection using Federated Learning Reef: A framework for collecting real- world vulnerabilities and fixes,

Reference 1

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verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.820957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.043079Z digest=sha256:85d47a573c278c949fa5d451ced61829dab8da0421eef9ec5b512b1fa69ba954

Observation d36ba755-fb74-4abd-9abb-de6d420ea335 · outbound

This paper cites Learning to locate and describe vulnerabilities,.

An Empirical Study of Vulnerability Detection using Federated Learning Learning to locate and describe vulnerabilities,

Reference 2

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verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.809987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.047403Z digest=sha256:fc42cc0681abea8cda0fa29bc1dbec07df9d4b3b4029cf0e2d89c2d0bf7e429e

Observation 091a0eeb-8c4e-419f-b638-e5914325add1 · outbound

This paper cites Toward improved deep learning-based vulnerability detection,.

An Empirical Study of Vulnerability Detection using Federated Learning Toward improved deep learning-based vulnerability detection,

Reference 3

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verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.799366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.051039Z digest=sha256:b53e2bf1a524a9a8ff9b996b54b540e26c443205df76fd0f1a407384cea2d2cd

Observation 3402a6d5-479a-4a4f-836a-b28dea2a562d · outbound

This paper cites Sysevr: A framework for using deep learning to detect software vulnerabilities,.

An Empirical Study of Vulnerability Detection using Federated Learning Sysevr: A framework for using deep learning to detect software vulnerabilities,

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.788628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.054855Z digest=sha256:f9a7e9ad2a0c805e4cca10a1c9048f544fe2bc81e6cf810d12c293c4407ed679

Observation eddc6e80-e92f-403b-8162-1909f21f9f61 · outbound

This paper cites Vuldeepecker: A deep learning-based system for vulnerability detection,.

An Empirical Study of Vulnerability Detection using Federated Learning Vuldeepecker: A deep learning-based system for vulnerability detection,

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.778598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.058338Z digest=sha256:c02fece9f7ac747c9a6c229ad50bab83a6c27316be066dcfaf83229a0256c8e3

Observation de096b10-4918-452d-974e-1fb69c3dcdc8 · outbound

This paper cites Vulchecker: Graph-based vulnerability localization in source code,.

An Empirical Study of Vulnerability Detection using Federated Learning Vulchecker: Graph-based vulnerability localization in source code,

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.768859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.063094Z digest=sha256:5b40cf59864106c05fef11578a5af8a526cf9a3ac4e0fb1a460d2dc248fc56c7

Observation 4e987683-27f9-4c08-98db-a7c949bae9d3 · outbound

This paper cites Vuldeelocator: A deep learning-based fine-grained vulnerability detector,.

An Empirical Study of Vulnerability Detection using Federated Learning Vuldeelocator: A deep learning-based fine-grained vulnerability detector,

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.758685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.067475Z digest=sha256:cb2317dda696c74f2d738081f4cc58d5c44cc1178d61cd7f2f2114522023a9d9

Observation 8f2cbc49-f55c-416c-87cf-0795ba4a6f19 · outbound

This paper cites Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks,.

An Empirical Study of Vulnerability Detection using Federated Learning Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks,

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.748671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.071411Z digest=sha256:646626a32748c520ba4f8535f4e8e76b079323483ad2e6bc7323005c22a06d5e

Observation d0e7eb0f-d966-4f81-be51-efb7b369f62b · outbound

This paper cites Deep learning based vulnerability detection: Are we there yet?.

An Empirical Study of Vulnerability Detection using Federated Learning Deep learning based vulnerability detection: Are we there yet?

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.739389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.074939Z digest=sha256:655d1b3c2253b6996ed0b33d0424b645e93fd68c601add8ff2d706689e6da06a

Observation dbf1130b-50a0-4e65-a2c4-8429ef8842c7 · outbound

This paper cites Coca: Improving and explaining graph neural network-based vulnerability detection systems,.

An Empirical Study of Vulnerability Detection using Federated Learning Coca: Improving and explaining graph neural network-based vulnerability detection systems,

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.729360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.079796Z digest=sha256:d23c1387c285d5cc753905ad896d796d9a479dac3c52a873b453665702482d9e

Observation dcfbe5eb-b5e7-4ab9-b323-cc3f985da6e8 · outbound

This paper cites Dataflow analysis-inspired deep learning for efficient vulnerability detection,.

An Empirical Study of Vulnerability Detection using Federated Learning Dataflow analysis-inspired deep learning for efficient vulnerability detection,

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.718738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.084037Z digest=sha256:7537c77b7536fe5d1a2b0d334b8c70fafe75d413973979bd5ee80e9655fe6026

Observation 376b899b-56a5-49c8-b090-c167a6a6adcb · outbound

This paper cites Reentrancy vulnerability detection and localization: A deep learning based two-phase approach,.

An Empirical Study of Vulnerability Detection using Federated Learning Reentrancy vulnerability detection and localization: A deep learning based two-phase approach,

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.707694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.088205Z digest=sha256:90306dc60781c3a4a847ecfb399aed1aa3bfacb8a94f7348772c472fcfcb1e4f

Observation fb047f66-df99-4b57-aaaa-1ae399c5d765 · outbound

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

An Empirical Study of Vulnerability Detection using Federated Learning Communication-efficient learning of deep networks from decentralized data,

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.696567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.091627Z digest=sha256:38b8d4fd5e0386d937379f1c536eb4a4fac556fd6011b0ab6f85c1881156405e

Observation 8cb1076f-1d6c-46f5-a963-4e060f79d9e6 · outbound

This paper cites Is aggregation the only choice? federated learning via layer-wise model recombination,.

An Empirical Study of Vulnerability Detection using Federated Learning Is aggregation the only choice? federated learning via layer-wise model recombination,

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.686362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.095676Z digest=sha256:4d773c29ba585c1cba5165c67a21aa1539f0a59df98749928208c74ee1c7008f

Observation e8005cef-194a-4404-b296-1b56e6da520f · outbound

This paper cites Federated optimization in heterogeneous networks,.

An Empirical Study of Vulnerability Detection using Federated Learning Federated optimization in heterogeneous networks,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.675285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.099052Z digest=sha256:1cdb0525010669971348484e4f02b6bef54ff879cd280d8566e2b79f72926c98

Observation 82024759-618d-456e-bd9f-c0772cb4ed0e · outbound

This paper cites Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark.

An Empirical Study of Vulnerability Detection using Federated Learning Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-12T13:37:19.102203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:37:19.102203Z digest=sha256:0b8ce539ac0e72a83e89b2e2c70bbefd6f43684696c7ed55505227141346c2fd

Observation fafad45a-ade1-4839-8593-d4064d28b260 · outbound

This paper cites Gitfl: Uncertainty-aware real-time asynchronous federated learning using version control,.

An Empirical Study of Vulnerability Detection using Federated Learning Gitfl: Uncertainty-aware real-time asynchronous federated learning using version control,

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.664819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.106064Z digest=sha256:398322601d7464d2fe2a06dc8661660d6283be672f9daa9a98050464b96fb48d

Observation b1f09d2f-69bd-400f-8c8a-e5c08f3db97c · outbound

This paper cites Vulnerability detection based on federated learning,.

An Empirical Study of Vulnerability Detection using Federated Learning Vulnerability detection based on federated learning,

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.654104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.109847Z digest=sha256:0b80b409be0f28de5399f527461351238c1104b52de5525385019da55a04aa81

Observation 671dcfd2-0ab5-46e5-a1c4-cd2bbf0d16cc · outbound

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

An Empirical Study of Vulnerability Detection using Federated Learning SCAFFOLD: Stochastic controlled averaging for federated learning,

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.643150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.113225Z digest=sha256:db3f8934f60f33be0aeffbd3e7423f6aab882d617b22982c5660320e702f0e25

Observation 3bc5f5dd-c157-47f1-b757-e8ea9ed49ce9 · outbound

This paper cites Diversevul: A new vulnerable source code dataset for deep learning based vulnerability detection,.

An Empirical Study of Vulnerability Detection using Federated Learning Diversevul: A new vulnerable source code dataset for deep learning based vulnerability detection,

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.633652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.117364Z digest=sha256:5a7c61b97180465fa361fd8ceed5efcd1ec7ea8734ae1960c2a94aa7904e4c88

Observation 34370cfd-0a46-4b46-b44b-6081d96f1c5d · outbound

This paper cites μvuldeepecker: A deep learning-based system for multiclass vulnerability detection,.

An Empirical Study of Vulnerability Detection using Federated Learning μvuldeepecker: A deep learning-based system for multiclass vulnerability detection,

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.623294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.127098Z digest=sha256:742430581417bc2366bd79ce661aa95fbeb234e05c23dee2eee9d1d0ac44a6fd

Observation 53d66d52-bbde-46bf-9580-0792fb5615ad · outbound

This paper cites VUDENC: vulnerability detection with deep learning on a natural codebase for python,.

An Empirical Study of Vulnerability Detection using Federated Learning VUDENC: vulnerability detection with deep learning on a natural codebase for python,

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.613763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.131064Z digest=sha256:177af7c71e67b304ecaa26bc49ee77e4b5a1b486beeac5368ed9126ae3a6dd2a

Observation e03b8303-ca39-4266-8e3b-0d237d1ceaac · outbound

This paper cites Vuldebert: A vulnerability detection system using BERT,.

An Empirical Study of Vulnerability Detection using Federated Learning Vuldebert: A vulnerability detection system using BERT,

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.603755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.134981Z digest=sha256:caaf81f501ac9a6fe278215a16a1ce870b25b246890b0485b73e078903277bf9

Observation cc34d97e-19f7-45ca-9cb3-6a9d215d255c · outbound

This paper cites Software vulnerability detection with gpt and in-context learning,.

An Empirical Study of Vulnerability Detection using Federated Learning Software vulnerability detection with gpt and in-context learning,

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.593407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.139106Z digest=sha256:b2e8692c0d654d155fb43e72d80b35a9899dcfc67b3565515ef83f789f4c0752

Observation 0647aea7-b6c4-4d02-83ca-98f5838118f1 · outbound

This paper cites Transformer-based language models for software vulnerability detection,.

An Empirical Study of Vulnerability Detection using Federated Learning Transformer-based language models for software vulnerability detection,

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.583265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.143542Z digest=sha256:01d557f4203bcf7b80a5ccf0b38c2e7605193a383492988218333f5b9fabf6a2

Observation 9cc5c1d5-09f5-475c-8449-15edd526695e · outbound

This paper cites Fedcross: Towards accurate federated learning via multi-model cross-aggregation,.

An Empirical Study of Vulnerability Detection using Federated Learning Fedcross: Towards accurate federated learning via multi-model cross-aggregation,

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.573050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.146937Z digest=sha256:f97edae91f77275ed0301d39d6652f32db0c6f3e0ea68e826bc234e2df08c222

Observation 792e6091-2e5b-48fa-a00e-4ac73b1e6202 · outbound

This paper cites Clustered sampling: Low-variance and improved representativity for clients selection in federated learning,.

An Empirical Study of Vulnerability Detection using Federated Learning Clustered sampling: Low-variance and improved representativity for clients selection in federated learning,

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.562182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.150591Z digest=sha256:fa3b0b525109cdb1c1bed0afde58a47fb6e5dddfa3a7304de40bd4d7689bb8cb

Observation 8939d94e-f5aa-4a31-8724-c12f8b7b13a6 · outbound

This paper cites Federated learning with soft clustering,.

An Empirical Study of Vulnerability Detection using Federated Learning Federated learning with soft clustering,

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.550551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.154584Z digest=sha256:b0f4188d780757f213c64164139718b1ac13644f0eab7b6b96512e5ec5e0edfe

Observation 5b8ce3a2-76e4-42c4-8d6f-03658f3142af · outbound

This paper cites Data-free knowledge distillation for heterogeneous federated learning,.

An Empirical Study of Vulnerability Detection using Federated Learning Data-free knowledge distillation for heterogeneous federated learning,

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.539062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.158553Z digest=sha256:b036e2f9ff16762061288f74e9dc4e5f9c025cd39fbfa1e15d77870803104029

Observation 670b9920-2f59-4a26-bdde-dc2cd3ad0f3f · outbound

This paper cites Fine-tuning global model via data-free knowledge distillation for non-iid federated learning,.

An Empirical Study of Vulnerability Detection using Federated Learning Fine-tuning global model via data-free knowledge distillation for non-iid federated learning,

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.528062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.161942Z digest=sha256:1ff847c9f4ee5478c6e0292f6cd7dc58af969e97ee4568ecf7d9eb09a7d1e745

Observation 7c09e2b2-dc3d-4a57-b5c8-8ebdb733ab16 · outbound

This paper cites Is Aggregation the Only Choice? Federated Learning via Layer-wise Model Recombination.

An Empirical Study of Vulnerability Detection using Federated Learning Is Aggregation the Only Choice? Federated Learning via Layer-wise Model Recombination

Reference 32

Resolution
verified exact
local_arxiv, observed 2026-08-12T13:37:19.338392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.165500Z digest=sha256:34277613c053077a27419f95a9642ebd28dd058d0e7e92d50e15f8c6197867e2

Observation 50831ff5-a928-4288-a238-87bb38929dfa · outbound

This paper cites FedMut: Generalized federated learning via stochastic mutation,.

An Empirical Study of Vulnerability Detection using Federated Learning FedMut: Generalized federated learning via stochastic mutation,

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.517094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.169675Z digest=sha256:4ad30c6919ef0f36cd3d91ab05f767354a0420d2b91b9795981dc17437f72506

Observation d3eb1d99-0019-4cf5-a7fb-23a62901bef8 · outbound

This paper cites Flexfl: Heterogeneous federated learning via apoz-guided flexible pruning in uncertain scenarios,.

An Empirical Study of Vulnerability Detection using Federated Learning Flexfl: Heterogeneous federated learning via apoz-guided flexible pruning in uncertain scenarios,

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.506490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.173271Z digest=sha256:f7b1b23b0d2d9a4174cf2d606f580dd8f2de3917f9640d531c2b11b0f235f812

Observation 7b7e855c-5f58-4705-8f3e-4be2d94f7899 · outbound

This paper cites Adaptivefl: Adaptive heterogeneous federated learning for resource-constrained aiot systems,.

An Empirical Study of Vulnerability Detection using Federated Learning Adaptivefl: Adaptive heterogeneous federated learning for resource-constrained aiot systems,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.495412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.176852Z digest=sha256:06237b02a31dfd5b130cbdf16756884df476355608768aad0db64e92d43fbbbf

Observation 0d43288f-c0c7-4e13-86cd-af697b3578dc · outbound

This paper cites End-to-end federated learning for autonomous driving vehicles,.

An Empirical Study of Vulnerability Detection using Federated Learning End-to-end federated learning for autonomous driving vehicles,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.484237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.180937Z digest=sha256:8bfcc8192a77442a1935ad657aebac3eab130752a2f81844a78732ed5a31378b

Observation 3ff75205-78f5-4a05-b333-db16292b2235 · outbound

This paper cites Federated learning for healthcare informatics,.

An Empirical Study of Vulnerability Detection using Federated Learning Federated learning for healthcare informatics,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.473184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.184770Z digest=sha256:b70e7acf28d707c351fb1755464f23cb5b6e75ef5a301d52c04a1316a33f2bcd

Observation 07fa9823-4789-4b30-9a65-4186838f521c · outbound

This paper cites BERT: pre-training of deep bidirectional transformers for language understanding,.

An Empirical Study of Vulnerability Detection using Federated Learning BERT: pre-training of deep bidirectional transformers for language understanding,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.462704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.187911Z digest=sha256:f719ceca1d13e5c4fb52ce39ee442483db3b379e53050ab293b11a0528c0fcc5

Observation 835ecf21-b227-46ef-a0fa-7326ad5273a3 · outbound

This paper cites Improving language understanding by generative pre- training,.

An Empirical Study of Vulnerability Detection using Federated Learning Improving language understanding by generative pre- training,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.451808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.191197Z digest=sha256:111e75296ecff4409a62fba1db5af04cbfb4a8198899965126e321c3b03151be

Observation 5be44936-37c0-4914-a197-e3ecc9015f7c · outbound

This paper cites OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization.

An Empirical Study of Vulnerability Detection using Federated Learning OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-12T13:37:19.194479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:37:19.194479Z digest=sha256:dc22987d2dd67c4b3a7f1941be0facfb4fa85787be9ff0154a9e9d95c1aa066f

Observation 90f39a71-7a86-4277-b600-8b33b1e3b430 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer,.

An Empirical Study of Vulnerability Detection using Federated Learning Exploring the limits of transfer learning with a unified text-to-text transformer,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.438988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.198167Z digest=sha256:206e28ac40d0e9a4cc7b132abfc01ea89b4c5dcee7b76e5751c6d1f09f955f68

Observation f05f3a7a-4797-4d57-9eb3-02f7add565da · outbound

This paper cites Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation,.

An Empirical Study of Vulnerability Detection using Federated Learning Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.426482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.201182Z digest=sha256:44c4b12c850934028a4d028ffc0ffee9325f3445f4fcc6250dcd004653a6d34b

Observation 326ae801-97f0-4a0f-87b3-0ae0e1a68bd3 · outbound

This paper cites Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning.

An Empirical Study of Vulnerability Detection using Federated Learning Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-12T13:37:19.204433Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:37:19.204433Z digest=sha256:f0984933a3c1a8a2edcdf8809f3907667c55f84097750e5560a59dbf4ce8af04

Observation f94388f6-b016-4e0d-943a-421034ef6845 · outbound

This paper cites GPT Understands, Too.

An Empirical Study of Vulnerability Detection using Federated Learning GPT Understands, Too

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-12T13:37:19.207883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:37:19.207883Z digest=sha256:11f39d01ed15c9fe167ad52d16b51bea8875ff9ebfbb6b513e229a3b10b1a36a

Observation d6325a51-89a6-43cc-b7c2-608341e63c45 · outbound

This paper cites P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks.

An Empirical Study of Vulnerability Detection using Federated Learning P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-12T13:37:19.211579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:37:19.211579Z digest=sha256:45904394964eb34f061e3d0211e5001a057441b4503e441ac74980c5bb456f4d

Observation a4ce2040-06bb-47a8-976d-00e92b239b8e · outbound

This paper cites LoRA: Low-rank adaptation of large language models,.

An Empirical Study of Vulnerability Detection using Federated Learning LoRA: Low-rank adaptation of large language models,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.414305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.216206Z digest=sha256:1a22fff4ba283a34ebd39b095a4c1a6ce77d94490cd60bc7f7be8b419f73cb09

Observation 59e7bc6f-f6f9-4969-bc85-3dbe073c77f7 · outbound

This paper cites FedPara: Low-Rank Hadamard Product for Communication-Efficient Federated Learning.

An Empirical Study of Vulnerability Detection using Federated Learning FedPara: Low-Rank Hadamard Product for Communication-Efficient Federated Learning

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-12T13:37:19.219657Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:37:19.219657Z digest=sha256:6d77cef2411abf351fae8a91bb57a0bab2e8df5f7c1a7ae590bf80ca75f85272

Observation a582428d-b5d3-44e6-8579-1f7468c6dc1d · outbound

This paper cites Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning,.

An Empirical Study of Vulnerability Detection using Federated Learning Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.402169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.223647Z digest=sha256:eca9afaa8904bce5afcd9f1cba8ea45f8db0ad6a2245119c88e7b2be453882cc

Observation 33784534-e098-46d1-b0da-2ef021a0a695 · outbound

This paper cites Model-contrastive federated learning,.

An Empirical Study of Vulnerability Detection using Federated Learning Model-contrastive federated learning,

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.389081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.227155Z digest=sha256:c43e878bf9cb17bfe15769eb8b7c9d4b2ca4e52b5a8e5a4281b746829b7b3fbc

Observation ad8e1323-795a-4203-a5b6-71b4ee0811e3 · outbound

This paper cites Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification.

An Empirical Study of Vulnerability Detection using Federated Learning Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-12T13:37:19.230669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:37:19.230669Z digest=sha256:8f75e89c739018f679477afdc7cbedec32a6047efab04840aa356dba0c9b4db0

Observation d7f7d308-4715-4b1c-95c6-a6a331e53c36 · outbound

This paper cites Language models are unsupervised multitask learners,.

An Empirical Study of Vulnerability Detection using Federated Learning Language models are unsupervised multitask learners,

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:37:19.376898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T13:37:19.234618Z digest=sha256:98523ff26e3cfd5890352264b873142bea54f61a8fba312830a45f4e5eef80a4

Pith citing papers

No inbound Pith citation observations are available.