Paper Citation Record · LEDGER

Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach

As of 9 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 1 inbound Pith citation observation for arXiv:2507.05685.

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

pith.paper-citation-record.v1
2507.05685 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:22:59.600017Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T10:19:16.463961Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T12:04:39.223431Z

Reference resolution

15 of 15 outbound references displayed

verified exact0
verified fuzzy9
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b0b58be0-e66e-48e2-8695-73ad524c1ce5 · outbound

This paper cites A survey on evaluation of large language models,.

Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach A survey on evaluation of large language models,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:59.788140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0e5d4cc2-a3c9-4e92-957b-a3003b70e9fe · outbound

This paper cites Split- fl: An efficient online federated learning framework with constrained computation and streaming data,.

Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach Split- fl: An efficient online federated learning framework with constrained computation and streaming data,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:59.777620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 477ce498-af79-4c3a-938d-3acd54647233 · outbound

This paper cites Outrageously large neural networks: The sparsely-gated mixture-of-experts layer,.

Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach Outrageously large neural networks: The sparsely-gated mixture-of-experts layer,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:59.766625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 790d63cd-ba7c-465a-ac82-2b8d3b155554 · outbound

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

Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach Communication-efficient learning of deep networks from decentralized data,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:59.754667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ace75a82-501b-4a5e-9aec-a08481b9cc7e · outbound

This paper cites Mixture-of-experts for distributed edge computing with channel-aware gating function,.

Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach Mixture-of-experts for distributed edge computing with channel-aware gating function,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:59.741920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ac833388-d2b6-4144-b686-954c466adbdc · outbound

This paper cites WDMoE: Wireless distributed large language models with mixture of experts,.

Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach WDMoE: Wireless distributed large language models with mixture of experts,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:59.729026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 666e18e6-e784-43a1-9cbc-0954de135e84 · outbound

This paper cites Client selection for wireless federated learn- ing with data and latency heterogeneity,.

Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach Client selection for wireless federated learn- ing with data and latency heterogeneity,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:59.717909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8fb2e2f9-843d-469e-9623-53f58f8020b5 · outbound

This paper cites Learning to Specialize: Joint Gating-Expert Training for Adaptive MoEs in Decentralized Settings.

Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach Learning to Specialize: Joint Gating-Expert Training for Adaptive MoEs in Decentralized Settings

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T19:22:59.572835Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 838d5775-75c1-413d-8836-7cbb3d896bca · outbound

This paper cites Federated Mixture of Experts.

Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach Federated Mixture of Experts

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T19:22:59.576797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6758104e-f222-48f2-a0a4-d5a97755a3f8 · outbound

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

Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach FedMoE-DA: Federated Mixture of Experts via Domain Aware Fine-grained Aggregation

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T19:22:59.580672Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39b2ebfe-28f6-4a87-8063-c68c533814d1 · outbound

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

Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Experts

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T19:22:59.585433Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24bc6e20-6904-4e19-9807-1f97a72c4bbe · outbound

This paper cites Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Models.

Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T19:22:59.589159Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c6f00b6-b444-439b-8477-640e37d109ea · outbound

This paper cites pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated Learning.

Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated Learning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T19:22:59.592924Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae351949-83f4-4b1a-8544-dd846b86e479 · outbound

This paper cites Beam prediction based on large language models,.

Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach Beam prediction based on large language models,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:59.705624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 939b0afa-bc6a-440f-8848-36bacd2b3234 · outbound

This paper cites Her research interests include machine learning, information retrieval, and data mining.

Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach Her research interests include machine learning, information retrieval, and data mining

Reference 2012

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:22:59.693686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

Observation 69d70836-7ecb-4619-bc8a-1c002179c6ec · inbound

Fisher-Routed Mixture of Experts for Federated Class-Incremental Learning cites this paper.

Fisher-Routed Mixture of Experts for Federated Class-Incremental Learning Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-06-30T12:04:39.224954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.