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Paper Citation Record · LEDGER

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge

As of 8 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 2 inbound Pith citation observations for arXiv:2508.18663.

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

pith.paper-citation-record.v1
2508.18663 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T16:22:25.583269Z

measured 43 of 43 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T09:29:03.234211Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T09:49:44.425417Z

Reference resolution

41 of 41 outbound references displayed

  • verified exact2
  • verified fuzzy7
  • unresolved32
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c774c6b6-66aa-4c63-ad25-4cd49238625d · outbound

This paper cites , " * write output.state after.block = add.period write newline.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge , " * write output.state after.block = add.period write newline

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-05T16:22:25.370442Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:22:25.370442Z digest=sha256:509f4b44616878748c62ca067bde055dacb7b9ce40674166794b5d5805012612

Observation 31915552-a4b9-4456-a22d-9d88b3b3d91f · outbound

This paper cites write newline.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge write newline

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-05T16:22:25.376117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:22:25.376117Z digest=sha256:0b3de24042b91f0a02b0779e5fd511a4bbb833df8ae84e39da36514d7e750085

Observation 238e4225-0935-4437-bc94-47db97f9ce79 · outbound

This paper cites an unresolved cited work.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge Unresolved cited work

Reference 3

Resolution
unresolved
raw_fallback, observed 2026-08-05T16:22:26.312571Z

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.

source=arxiv_source observed=2026-08-05T16:22:25.381979Z digest=sha256:05289c5085e4d4385e0cd1410724415bcbdb6f9d695a613821b2606b96dd231e

Observation e0d5e685-6c9e-4a4e-81c4-aab92c0784d2 · outbound

This paper cites Scalable Artificial Intelligence for Science: Perspectives, Methods and Exemplars.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge Scalable Artificial Intelligence for Science: Perspectives, Methods and Exemplars

Reference 4

Resolution
verified exact
local_arxiv, observed 2026-08-05T16:22:25.747326Z

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.

source=arxiv_source observed=2026-08-05T16:22:25.387885Z digest=sha256:d2724b6ee7f37974bdc1f83da1ac15b3592bbec5228ebeca4a3388599df13bdf

Observation ab0ed24a-d37e-42ed-a734-01786517ac25 · outbound

This paper cites X.; and Xu, M.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge X.; and Xu, M

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T16:22:26.295258Z

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.

source=arxiv_source observed=2026-08-05T16:22:25.394438Z digest=sha256:efb896529795942ba38b35f3b4fa939706c200e9beb45ed39ce81ec0a42bdd23

Observation 792d9617-3b4b-494d-b41c-9fa1cef38bc8 · outbound

This paper cites an unresolved cited work.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge Unresolved cited work

Reference 6

Resolution
unresolved
raw_fallback, observed 2026-08-05T16:22:26.275432Z

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.

source=arxiv_source observed=2026-08-05T16:22:25.401083Z digest=sha256:a2bdb453c8c85662a887fc21234796e9148e7a313633749e19e7016dea632b52

Observation 13cb8eb2-2951-40f5-99a1-3af6ab6ef283 · outbound

This paper cites an unresolved cited work.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge Unresolved cited work

Reference 7

Resolution
unresolved
raw_fallback, observed 2026-08-05T16:22:26.255931Z

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.

source=arxiv_source observed=2026-08-05T16:22:25.406705Z digest=sha256:58a23f0fa06d2ea474b34cbaab997b548e9e0728f964a7d7bc06a8d06024fa69

Observation 568312da-4501-4183-9090-1d88fbb90b90 · outbound

This paper cites an unresolved cited work.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge Unresolved cited work

Reference 8

Resolution
unresolved
raw_fallback, observed 2026-08-05T16:22:26.239120Z

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.

source=arxiv_source observed=2026-08-05T16:22:25.412702Z digest=sha256:348ce9508a46f1b1dbec23b5b7eafe2d412f62b5ca117c0d31a09c6b9876c8da

Observation cd58f7a6-7d79-41a2-bd6e-166b9a9e903a · outbound

This paper cites Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-05T16:22:25.418475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:22:25.418475Z digest=sha256:ce0c12a46ee3e46390ec8e321659e7ae0b0d44444fafb01974614ec1856aa888

Observation ceeac16c-a177-408b-b455-c5e0590730c7 · outbound

This paper cites an unresolved cited work.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge Unresolved cited work

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-05T16:22:25.424730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:22:25.424730Z digest=sha256:a4f46c2f9f56bbe313f2945040b6e3c5d7fa3f509056a84af80368c57315f3ca

Observation 07bc5713-0f55-490b-a425-238384592d25 · outbound

This paper cites G.; and Goldstein, T.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge G.; and Goldstein, T

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T16:22:26.208597Z

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.

source=arxiv_source observed=2026-08-05T16:22:25.430323Z digest=sha256:d775726b54d1b57f1ad085dcdd64469230764efbdda8dd0f217be2f299b217a2

Observation bbfdd448-49ec-422a-96b8-e6dbe5b93757 · outbound

This paper cites an unresolved cited work.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge Unresolved cited work

Reference 12

Resolution
unresolved
raw_fallback, observed 2026-08-05T16:22:26.189564Z

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.

source=arxiv_source observed=2026-08-05T16:22:25.436388Z digest=sha256:2fe6f65b3a00a211d8a1add2ec55272f38204e271bb78695e951632733e3895c

Observation 1b76d721-b10f-47d9-a92b-e13ecabcfed3 · outbound

This paper cites J.; yelong shen; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; and Chen, W.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge J.; yelong shen; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; and Chen, W

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T16:22:26.172054Z

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.

source=arxiv_source observed=2026-08-05T16:22:25.441620Z digest=sha256:1223a7b9ef2777a96aa6f477af617316bbc20c0e1c3ac43ecbb86941280a16d4

Observation ce49f988-2075-4a24-b4a1-a6b8283b8947 · outbound

This paper cites an unresolved cited work.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge Unresolved cited work

Reference 14

Resolution
unresolved
raw_fallback, observed 2026-08-05T16:22:26.153925Z

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.

source=arxiv_source observed=2026-08-05T16:22:25.446452Z digest=sha256:246aee0b281c818f12e525700dfa522d038fb3d30b113991974c83f550b43299

Observation cbef3a7f-c6c2-44a7-b34f-db56cd5d7139 · outbound

This paper cites B.; Avent, B.; Bellet, A.; Bennis, M.; Bhagoji, A.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge B.; Avent, B.; Bellet, A.; Bennis, M.; Bhagoji, A

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T16:22:26.136200Z

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.

source=arxiv_source observed=2026-08-05T16:22:25.450949Z digest=sha256:bc0c1b1a483823bc99d19af87cd43fc36304d505a872218ff954be5cf44db49e

Observation f5a378d7-9d77-449e-bdbe-739ef60a7bff · outbound

This paper cites an unresolved cited work.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge Unresolved cited work

Reference 16

Resolution
unresolved
raw_fallback, observed 2026-08-05T16:22:26.117401Z

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.

source=arxiv_source observed=2026-08-05T16:22:25.456020Z digest=sha256:9ba790d9d33111e63c3cf3f557b020240abc4dfdf115e16987fdce9247ea7fc2

Observation 9fa7398f-3b77-4206-84f2-21d2f6d7868f · outbound

This paper cites an unresolved cited work.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge Unresolved cited work

Reference 17

Resolution
unresolved
raw_fallback, observed 2026-08-05T16:22:26.100804Z

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.

source=arxiv_source observed=2026-08-05T16:22:25.460963Z digest=sha256:51962858bfe93f7e8e22fc229ebd7e27be2d5ff597a27798ad232a75df27e34d

Observation 26566256-e179-42ae-a809-5bc3d060fa88 · outbound

This paper cites an unresolved cited work.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge Unresolved cited work

Reference 18

Resolution
unresolved
raw_fallback, observed 2026-08-05T16:22:26.083018Z

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.

source=arxiv_source observed=2026-08-05T16:22:25.465906Z digest=sha256:604d9538b3a70869f9054c815cb035e9ae167580ef539bb91b8ecc28e1019838

Observation c8e21d53-fa6d-4e5a-9db4-c6ac92ae16dc · outbound

This paper cites Resource-Efficient Federated Fine-Tuning Large Language Models for Heterogeneous Data.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge Resource-Efficient Federated Fine-Tuning Large Language Models for Heterogeneous Data

Reference 19

Resolution
verified exact
local_arxiv, observed 2026-08-05T16:22:25.703254Z

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.

source=arxiv_source observed=2026-08-05T16:22:25.471054Z digest=sha256:4b7ad94af8d22cebed82133caa42a1d9a7c215fc738cf8a60d1001b6c97dc1c8

Observation 89c28913-22c7-4fef-8d13-344e92ef65cd · outbound

This paper cites an unresolved cited work.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge Unresolved cited work

Reference 20

Resolution
unresolved
raw_fallback, observed 2026-08-05T16:22:26.065797Z

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.

source=arxiv_source observed=2026-08-05T16:22:25.478177Z digest=sha256:c684016759db5f1dc71230d5af171c569b03e0935796d22904bee0a0174f0070

Observation 66c642d7-1089-4797-a0a6-f77d7fc6e08f · outbound

This paper cites FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Experts.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Experts

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-05T16:22:25.483701Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:22:25.483701Z digest=sha256:e68dea30762edf083958ae9413aa7b069091d872f8522b6053fc0dc37c3a2ed2

Observation aa3a1092-0954-4e85-a58b-6f398cb810b4 · outbound

This paper cites K.; Jonnalagadda, A.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge K.; Jonnalagadda, A

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T16:22:26.048018Z

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.

source=arxiv_source observed=2026-08-05T16:22:25.490289Z digest=sha256:3a69dfedc0898932bda92bc65c68ed7c104fc00fd4d82ab661b26461fb703887

Observation 64d21c20-29aa-459b-b7b8-90d4eb5d7736 · outbound

This paper cites an unresolved cited work.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge Unresolved cited work

Reference 23

Resolution
unresolved
raw_fallback, observed 2026-08-05T16:22:26.029087Z

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.

source=arxiv_source observed=2026-08-05T16:22:25.495083Z digest=sha256:fa69f95d1d41c059a665595aa17dd5dbcc6e551390dc78a87512a6a94692d433

Observation f7eef584-1da0-4299-a807-e5d99202b3f2 · outbound

This paper cites an unresolved cited work.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge Unresolved cited work

Reference 24

Resolution
unresolved
raw_fallback, observed 2026-08-05T16:22:26.003149Z

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.

source=arxiv_source observed=2026-08-05T16:22:25.499731Z digest=sha256:c23c8132a2bf0d07647c61a7aa2564b480196f748598b490154bd8f18b15b85d

Observation 71abbc87-b442-455b-bf3b-c7c1a9b53ed0 · outbound

This paper cites an unresolved cited work.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge Unresolved cited work

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-05T16:22:25.504764Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:22:25.504764Z digest=sha256:23c608ebfd7960fc86acf7d494ff29828686ddd0797fae65cae7bcfd6cb11004

Observation ae414031-2a40-400a-8591-5a39e89030a4 · outbound

This paper cites an unresolved cited work.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge Unresolved cited work

Reference 26

Resolution
unresolved
raw_fallback, observed 2026-08-05T16:22:25.971355Z

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.

source=arxiv_source observed=2026-08-05T16:22:25.509317Z digest=sha256:d2f27539e4b388516cd128e24579224d1b0e25d88573b1729704d6563b377ee4

Observation 49c03f7c-cbc7-46e5-b151-99d93cb39389 · outbound

This paper cites an unresolved cited work.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge Unresolved cited work

Reference 27

Resolution
unresolved
raw_fallback, observed 2026-08-05T16:22:25.955528Z

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.

source=arxiv_source observed=2026-08-05T16:22:25.514028Z digest=sha256:d0d3e431c4cf262ba9428bb705b7b55d81a1031fe1cd2d6e70ba5e9b1681e9e6

Observation 3765bc65-ac7d-4b75-8293-899abc884a9b · outbound

This paper cites H.; and Pham, V.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge H.; and Pham, V

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T16:22:25.938836Z

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.

source=arxiv_source observed=2026-08-05T16:22:25.519063Z digest=sha256:e14d2e1b46c44ce3704f7195e796985cce63f3cf41a269d6e49e046b1245de55

Observation 5691ae14-8cd8-4cac-887a-102267721cde · outbound

This paper cites an unresolved cited work.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge Unresolved cited work

Reference 29

Resolution
unresolved
raw_fallback, observed 2026-08-05T16:22:25.919811Z

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.

source=arxiv_source observed=2026-08-05T16:22:25.523777Z digest=sha256:e0942ab6750be10c8fb0d375ed7a481b464f20adfa97925b7bc3430c53ca1489

Observation 33b8518d-da2f-447d-a858-356dd09d21fd · outbound

This paper cites an unresolved cited work.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge Unresolved cited work

Reference 30

Resolution
unresolved
raw_fallback, observed 2026-08-05T16:22:25.902965Z

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.

source=arxiv_source observed=2026-08-05T16:22:25.528328Z digest=sha256:1e57a6146d9160c6eab6d8981130118692a91183e975036acc2cc92437eabadb

Observation 093a9f81-8683-4e1b-99bf-e02c5beb27d0 · outbound

This paper cites an unresolved cited work.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge Unresolved cited work

Reference 31

Resolution
unresolved
raw_fallback, observed 2026-08-05T16:22:25.886911Z

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.

source=arxiv_source observed=2026-08-05T16:22:25.533603Z digest=sha256:8deb5927be96e4a521c7b9aa57093cfe7bc4384e3375e859e88ad4cbe6b78912

Observation 2f3d38ee-a558-4ccb-b7e9-40d4391afc26 · outbound

This paper cites FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-05T16:22:25.538325Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:22:25.538325Z digest=sha256:9188d1260aa5b2d0702ccce92d36fc87315e7518180216c26b4c522091a2ce46

Observation be730c2f-2d52-454d-ad5b-17a377dd6e3a · outbound

This paper cites an unresolved cited work.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge Unresolved cited work

Reference 33

Resolution
unresolved
raw_fallback, observed 2026-08-05T16:22:25.870688Z

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.

source=arxiv_source observed=2026-08-05T16:22:25.543751Z digest=sha256:8b09b3a26c4bc2e8c8897da17bff0ad763a18135e9de33eab6bf32a4d60b28c4

Observation 15e700d4-e4e0-4846-93d3-38fd702f1e52 · outbound

This paper cites FedMoE-DA: Federated Mixture of Experts via Domain Aware Fine-grained Aggregation.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge FedMoE-DA: Federated Mixture of Experts via Domain Aware Fine-grained Aggregation

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-05T16:22:25.548290Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 9c2e49c8-db44-4c50-93a5-8bdbec7997b6 · outbound

This paper cites an unresolved cited work.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge Unresolved cited work

Reference 35

Resolution
unresolved
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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.

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Observation e88f9537-4fee-486e-a007-f201d0ed2627 · outbound

This paper cites an unresolved cited work.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge Unresolved cited work

Reference 36

Resolution
unresolved
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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.

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Observation d5ccaac9-237d-422e-bf55-b4db6ab09198 · outbound

This paper cites an unresolved cited work.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge Unresolved cited work

Reference 37

Resolution
unresolved
raw_fallback, observed 2026-08-05T16:22:25.820726Z

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.

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Observation 3cd456ab-afb0-49f8-9590-f544e98381a0 · outbound

This paper cites an unresolved cited work.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge Unresolved cited work

Reference 38

Resolution
unresolved
raw_fallback, observed 2026-08-05T16:22:25.804657Z

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.

source=arxiv_source observed=2026-08-05T16:22:25.568691Z digest=sha256:9c5edb2fa03272dfd05570606fe5ac22f5829a7063a017e44c524ff73ca17078

Observation f1596c53-921a-4b73-95b0-c567aa4af1ee · outbound

This paper cites A Survey of Large Language Models.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge A Survey of Large Language Models

Reference 39

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:22:25.573331Z digest=sha256:0c9436e2c8e91b6229edfeefc2c73248f457da6a9294c0000725b7c7787b416b

Observation 8fce0adf-c42b-4565-a7d7-142becf0c521 · outbound

This paper cites M.; Chen, z.; Le, Q.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge M.; Chen, z.; Le, Q

Reference 40

Resolution
verified fuzzy
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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.

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Observation c97ff0c6-d97a-4ad1-b0f6-00438566c029 · outbound

This paper cites an unresolved cited work.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge Unresolved cited work

Reference 41

Resolution
unresolved
raw_fallback, observed 2026-08-05T16:22:25.765971Z

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.

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Pith citing papers

Observation 4048f409-845f-49f5-b452-5a8b11168b3e · inbound

Enhancing Cross-Problem Vehicle Routing via Federated Learning cites this paper.

Enhancing Cross-Problem Vehicle Routing via Federated Learning FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-05-11T10:06:02.383975Z

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.

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Observation ce84f098-2229-45e3-ae9b-e756142a298f · inbound

Priority-Aware Learning-Unlearning Correction for Dynamic Decentralized LoRA Fine-Tuning cites this paper.

Priority-Aware Learning-Unlearning Correction for Dynamic Decentralized LoRA Fine-Tuning FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge

Reference 51

Resolution
verified exact
arxiv_id, observed 2026-07-04T09:49:44.427045Z

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.

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