Paper Citation Record · LEDGER

Memory-Efficient Split Federated Learning for LLM Fine-Tuning on Heterogeneous Mobile Devices

As of 8 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 1 inbound Pith citation observation for arXiv:2506.02940.

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

pith.paper-citation-record.v1
2506.02940 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:18:09.689856Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:16:14.936447Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T21:16:15.280756Z

Reference resolution

19 of 19 outbound references displayed

verified exact0
verified fuzzy16
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5f8efae9-37cf-44c5-9574-7cc6e14c5cce · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Memory-Efficient Split Federated Learning for LLM Fine-Tuning on Heterogeneous Mobile Devices BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T11:18:07.821552Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fcf93855-d4cf-431b-9f27-34bc1bb48119 · outbound

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

Memory-Efficient Split Federated Learning for LLM Fine-Tuning on Heterogeneous Mobile Devices Improving language understanding by gener ative pre- training,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:18:12.896182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 52a788fb-6bf0-4c7a-93fa-9bf84e2cb9a1 · outbound

This paper cites Masked autoencoders are scalable vision learners,.

Memory-Efficient Split Federated Learning for LLM Fine-Tuning on Heterogeneous Mobile Devices Masked autoencoders are scalable vision learners,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:18:12.763747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ef84e7a2-a5c9-44be-bae9-6ced639c0380 · outbound

This paper cites Holis tic network virtualization and pervasive network intelligenc e for 6G,.

Memory-Efficient Split Federated Learning for LLM Fine-Tuning on Heterogeneous Mobile Devices Holis tic network virtualization and pervasive network intelligenc e for 6G,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:18:12.632613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4c708f01-c636-4b85-aa0c-083dfa7f3f78 · outbound

This paper cites AI-native network slicing for 6G networks,.

Memory-Efficient Split Federated Learning for LLM Fine-Tuning on Heterogeneous Mobile Devices AI-native network slicing for 6G networks,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:18:12.413536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 83767adc-5162-440c-9600-26665adbe40c · outbound

This paper cites Ener gy- efﬁcient cooperative task ofﬂoading in NOMA-enabled vehic ular fog computing,.

Memory-Efficient Split Federated Learning for LLM Fine-Tuning on Heterogeneous Mobile Devices Ener gy- efﬁcient cooperative task ofﬂoading in NOMA-enabled vehic ular fog computing,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:18:12.183579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 33df8127-a7b9-4e50-9c56-87f887ff2f34 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Memory-Efficient Split Federated Learning for LLM Fine-Tuning on Heterogeneous Mobile Devices LoRA: Low-Rank Adaptation of Large Language Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T11:18:08.343158Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5d0cc26-37a7-44c4-8fc2-f114f41cebb5 · outbound

This paper cites Efﬁcient feder ated learning for modern NLP,.

Memory-Efficient Split Federated Learning for LLM Fine-Tuning on Heterogeneous Mobile Devices Efﬁcient feder ated learning for modern NLP,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:18:11.932745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7d25c3cd-7317-44db-a7d2-ce7e6672390e · outbound

This paper cites Federated ﬁne-tuning for pre-trained foundation models over wireless networks,.

Memory-Efficient Split Federated Learning for LLM Fine-Tuning on Heterogeneous Mobile Devices Federated ﬁne-tuning for pre-trained foundation models over wireless networks,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:18:11.661531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 498d908c-8f73-4d41-a8a7-859b2b8efe6c · outbound

This paper cites To talk or t o work: Flexible communication compression for energy efﬁci ent feder- ated learning over heterogeneous mobile edge devices,.

Memory-Efficient Split Federated Learning for LLM Fine-Tuning on Heterogeneous Mobile Devices To talk or t o work: Flexible communication compression for energy efﬁci ent feder- ated learning over heterogeneous mobile edge devices,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:18:11.367097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c9947fc4-c61f-41bc-bbba-f4595b5780bc · outbound

This paper cites FedFMSL: Federated learning of foundation models with spa rsely activated LoRA,.

Memory-Efficient Split Federated Learning for LLM Fine-Tuning on Heterogeneous Mobile Devices FedFMSL: Federated learning of foundation models with spa rsely activated LoRA,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:18:11.209549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9ad9ebbc-9619-477e-a89a-f108bb2cccc5 · outbound

This paper cites Failure-resil ient distributed inference with model compression over heterogeneous edge d evices,.

Memory-Efficient Split Federated Learning for LLM Fine-Tuning on Heterogeneous Mobile Devices Failure-resil ient distributed inference with model compression over heterogeneous edge d evices,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:18:11.036422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e21b1115-a137-4b20-97b0-1b806a063dc1 · outbound

This paper cites Make pre-trained model rev ersible: From parameter to memory efﬁcient ﬁne-tuning,.

Memory-Efficient Split Federated Learning for LLM Fine-Tuning on Heterogeneous Mobile Devices Make pre-trained model rev ersible: From parameter to memory efﬁcient ﬁne-tuning,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:18:10.846554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5aad1700-beec-436d-a890-12a64535c1b0 · outbound

This paper cites FedBER T: When federated learning meets pre-training,.

Memory-Efficient Split Federated Learning for LLM Fine-Tuning on Heterogeneous Mobile Devices FedBER T: When federated learning meets pre-training,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:18:10.668664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 83bcccf6-7a59-4289-aeb7-ccb96a6adc52 · outbound

This paper cites SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Models.

Memory-Efficient Split Federated Learning for LLM Fine-Tuning on Heterogeneous Mobile Devices SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T11:18:09.225536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c336a106-675a-471b-870c-369f558b50dd · outbound

This paper cites Delay-optimal distributed edge computing in wireless edge networks,.

Memory-Efficient Split Federated Learning for LLM Fine-Tuning on Heterogeneous Mobile Devices Delay-optimal distributed edge computing in wireless edge networks,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:18:10.479574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 44b8d0a8-4b4d-49e3-b6bf-14766090b5ed · outbound

This paper cites CARER: Contextualized affect representations for emotion recogn ition,.

Memory-Efficient Split Federated Learning for LLM Fine-Tuning on Heterogeneous Mobile Devices CARER: Contextualized affect representations for emotion recogn ition,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:18:10.256016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4c242fdd-6606-446a-91cf-94823327f137 · outbound

This paper cites Split learning over wireless networks: Parallel design an d resource management,.

Memory-Efficient Split Federated Learning for LLM Fine-Tuning on Heterogeneous Mobile Devices Split learning over wireless networks: Parallel design an d resource management,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:18:10.068466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 14a54d69-5701-4f65-9a9e-b35936a56c6a · outbound

This paper cites Energy harvesting space-a ir-sea inte- grated networks for MEC-enabled maritime internet of thing s,.

Memory-Efficient Split Federated Learning for LLM Fine-Tuning on Heterogeneous Mobile Devices Energy harvesting space-a ir-sea inte- grated networks for MEC-enabled maritime internet of thing s,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:18:09.895455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

Observation 41b5aee5-88ed-4615-a396-5a8d3600cb15 · inbound

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration cites this paper.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Memory-Efficient Split Federated Learning for LLM Fine-Tuning on Heterogeneous Mobile Devices

Reference 140

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:16:15.283528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.