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Paper Citation Record · LEDGER

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge

As of 7 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 1 inbound Pith citation observation for arXiv:2601.00549.

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

pith.paper-citation-record.v1
2601.00549 v3

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T13:09:40.864362Z

measured 37 of 37 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 2026-05-10T08:10:49.967690Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T08:12:26.601703Z

Reference resolution

36 of 36 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved36
  • parse uncertain0
  • malformed identifier0
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External citation measurements

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Outbound references

Observation 7d3a1a61-3a4a-4a31-a1e4-18e563ace0f4 · outbound

This paper cites Task-oriented 6G native-AI network architecture,.

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge Task-oriented 6G native-AI network architecture,

Reference 1

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source=pdf_text observed=2026-08-03T13:09:36.946392Z digest=sha256:76c95be9a316b7bb166590aa85cb7556e8681e7f57e889b003a794c0a67f66ca

Observation 65d7dc84-194b-410a-b256-de1754a544a0 · outbound

This paper cites Explainable AI in 6G O-RAN: A tutorial and survey on architecture, use cases, challenges, and future research,.

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge Explainable AI in 6G O-RAN: A tutorial and survey on architecture, use cases, challenges, and future research,

Reference 2

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source=pdf_text observed=2026-08-03T13:09:37.160567Z digest=sha256:63797944368f1b8d3c83fb92b967c51419581a3dcf8d8c57ef0b335441cee2ba

Observation bc8cde13-0dd2-47d6-a079-91831faf6048 · outbound

This paper cites Toward 6g native-AI network: Foun- dation model-based cloud-edge-end collaboration framework,.

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge Toward 6g native-AI network: Foun- dation model-based cloud-edge-end collaboration framework,

Reference 3

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Observation 17f5ce63-06dc-4848-91f0-35d80e893729 · outbound

This paper cites Intellicise wireless networks from semantic communications: A survey, research issues, and challenges,.

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge Intellicise wireless networks from semantic communications: A survey, research issues, and challenges,

Reference 4

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source=pdf_text observed=2026-08-03T13:09:37.499143Z digest=sha256:d0b2d31703e3b3bfcd309153c35798989961bd6252cbf8522674c2db75d5b75f

Observation 69fdcf10-fb5f-4f77-8423-8866261beedf · outbound

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

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge Communication-efficient learning of deep networks from decentralized data,

Reference 5

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source=pdf_text observed=2026-08-03T13:09:37.735529Z digest=sha256:4a537a5e30bf435e814f506525386acb6318fc8bad56784b9a3405d00da234d1

Observation 981c0953-24d5-4f50-b574-167cc0ba01fb · outbound

This paper cites A comprehensive tutorial and survey of O-RAN: Exploring slicing-aware architecture, deployment options, use cases, and challenges,.

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge A comprehensive tutorial and survey of O-RAN: Exploring slicing-aware architecture, deployment options, use cases, and challenges,

Reference 6

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Observation 321973f8-0c8b-4860-a9df-f11c59d82487 · outbound

This paper cites Scheduling and aggregation design for asynchronous federated learning over wireless networks,.

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge Scheduling and aggregation design for asynchronous federated learning over wireless networks,

Reference 7

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Observation 44f3153f-bbeb-4562-8a0a-393bb28ba68c · outbound

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

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge Lora: Low-rank adaptation of large language models,

Reference 8

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source=pdf_text observed=2026-08-03T13:09:38.214584Z digest=sha256:401e43eb32b2a541e9ab54f6a1c4612a0dc24de9d1cc07a4ecac2cc2fd711d0f

Observation ed3426bc-d9d7-4e0c-af86-2c080b418d87 · outbound

This paper cites Galore: Memory-Efficient LLM training by gradient low-rank projection,.

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge Galore: Memory-Efficient LLM training by gradient low-rank projection,

Reference 9

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Observation f1fab0ac-e9f5-4f86-ac4b-51e143ac73f4 · outbound

This paper cites A comprehensive survey on communication-efficient federated learning in mobile edge environments,.

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge A comprehensive survey on communication-efficient federated learning in mobile edge environments,

Reference 10

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Observation fea64e86-cd14-4206-8a94-ff986a51c1d9 · outbound

This paper cites Low-rank adaptation for foundation models: A com- prehensive review,.

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge Low-rank adaptation for foundation models: A com- prehensive review,

Reference 11

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Observation 792fb8a8-d411-4a0c-9ada-55cab5fec6c2 · outbound

This paper cites A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning.

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning

Reference 12

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Observation 958b2869-1769-4913-84ef-5d291efe4241 · outbound

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

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning,

Reference 13

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Observation 03f70c8f-8536-4551-89bb-def4b1927429 · outbound

This paper cites BitFit: Simple parameter- efficient fine-tuning for transformer-based masked language-models,.

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge BitFit: Simple parameter- efficient fine-tuning for transformer-based masked language-models,

Reference 14

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Observation 64ed5d56-862f-4027-a306-8846c3139c9f · outbound

This paper cites Loraprune: Structured pruning meets low-rank parameter-efficient fine-tuning,.

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge Loraprune: Structured pruning meets low-rank parameter-efficient fine-tuning,

Reference 15

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source=pdf_text observed=2026-08-03T13:09:38.713736Z digest=sha256:8db8824521daf0ef0e57bd28449b7893f765debb8834827673c98a8ad588ec8f

Observation b2a98b6e-6111-4f3d-94f7-446c43b7325a · outbound

This paper cites Adaptive budget allocation for parameter-efficient fine-tuning,.

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge Adaptive budget allocation for parameter-efficient fine-tuning,

Reference 16

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source=pdf_text observed=2026-08-03T13:09:38.771844Z digest=sha256:94353a6cc4f844abb1b10969f834a2645e370e5b24240342b38ba8b172482d19

Observation 745d2d1c-3635-45d4-8d0d-734dcc49da04 · outbound

This paper cites Flora: Low-rank adapters are secretly gradient compressors,.

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge Flora: Low-rank adapters are secretly gradient compressors,

Reference 17

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source=pdf_text observed=2026-08-03T13:09:38.897269Z digest=sha256:4ec1561e9ff6b0178409f573b754e6786c8b7b3e45d005684a793e99f15f49f6

Observation d6229ec2-e723-4f61-bc42-bb8791e977d3 · outbound

This paper cites Design and analysis of uplink and downlink communications for federated learning,.

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge Design and analysis of uplink and downlink communications for federated learning,

Reference 18

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Observation f24b2967-c57e-45c4-bb47-7a01ec21457e · outbound

This paper cites High-dimensional stochastic gradient quantization for communication-efficient edge learning,.

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge High-dimensional stochastic gradient quantization for communication-efficient edge learning,

Reference 19

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Observation 495a7536-71c0-468f-a454-84d20085db81 · outbound

This paper cites Fed-QSSL: A framework for personalized federated learning under bitwidth and data heterogeneity,.

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge Fed-QSSL: A framework for personalized federated learning under bitwidth and data heterogeneity,

Reference 20

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source=pdf_text observed=2026-08-03T13:09:39.209097Z digest=sha256:ec69b6108fb8063833c0933f1f430d52a9b39a09b5697ef450dd5275cf84e9e0

Observation 48f8cac7-8514-454b-90db-9be551f95398 · outbound

This paper cites Deep anomaly detection for time-series data in industrial IoT: A communication-efficient on-device federated learning approach,.

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge Deep anomaly detection for time-series data in industrial IoT: A communication-efficient on-device federated learning approach,

Reference 21

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source=pdf_text observed=2026-08-03T13:09:39.337939Z digest=sha256:d25643c24906fb27503c38c64aa598adfeeb39aa6d0329f505eac40506197768

Observation c175c43e-2c82-475e-abff-fedff32e0773 · outbound

This paper cites A distributed synchronous SGD algorithm with global Top-k sparsification for low bandwidth networks,.

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge A distributed synchronous SGD algorithm with global Top-k sparsification for low bandwidth networks,

Reference 22

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Observation 7bac6a8d-7a61-4414-b5f2-50a509683031 · outbound

This paper cites Latency-efficient wireless federated learning with sparsification and quantization for heterogeneous devices,.

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge Latency-efficient wireless federated learning with sparsification and quantization for heterogeneous devices,

Reference 23

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source=pdf_text observed=2026-08-03T13:09:39.544734Z digest=sha256:767dc1503686aa88722b8079e3e6b362f13af08eaa47f1190e53ac0df7621166

Observation fcab185e-9394-4360-9e05-0927f24384ed · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale,.

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 24

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Observation 78d1e30a-ce9d-47c0-8acb-8767f6113482 · outbound

This paper cites Parameter-efficient transfer learning for NLP,.

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge Parameter-efficient transfer learning for NLP,

Reference 25

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source=pdf_text observed=2026-08-03T13:09:39.739559Z digest=sha256:9a6386f1dcb934b0a9b5505ac4f176631ae16a6e91dd86481cafc830df930231

Observation cfabf06e-b548-4783-b04e-cbc07ac2ed3a · outbound

This paper cites Unsupervised AoA estimation based on dual-path knowledge-aware auto-encoders,.

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge Unsupervised AoA estimation based on dual-path knowledge-aware auto-encoders,

Reference 26

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Observation 57b73f2a-07ab-40a3-a768-df78700b55ce · outbound

This paper cites Understanding Self-supervised Learning with Dual Deep Networks.

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge Understanding Self-supervised Learning with Dual Deep Networks

Reference 27

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Observation 590473f7-f936-48b1-ba92-9ace368d88e0 · outbound

This paper cites Direct localization for massive MIMO,.

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge Direct localization for massive MIMO,

Reference 28

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Observation f9dc6521-854e-4b51-8fff-3799d80fc932 · outbound

This paper cites An elementary proof of a theorem of Johnson and Lindenstrauss,.

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge An elementary proof of a theorem of Johnson and Lindenstrauss,

Reference 29

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Observation 5671efb3-3001-4a74-aee3-9cbecc907fbd · outbound

This paper cites Study on artificial intelligence (AI)/machine learning (ML) for NR air interface (Release 18),.

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge Study on artificial intelligence (AI)/machine learning (ML) for NR air interface (Release 18),

Reference 30

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Observation 4e26caa2-9cc9-4e2f-aaac-2a98fdc6e4af · outbound

This paper cites A simplified parametric channel estimation scheme for OFDM systems,.

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge A simplified parametric channel estimation scheme for OFDM systems,

Reference 31

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Observation 67fa5d42-ee37-419c-b0ad-283400a7c83d · outbound

This paper cites MUSIC, maximum likelihood and Cramer- Rao bound: Further results and comparisons,.

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge MUSIC, maximum likelihood and Cramer- Rao bound: Further results and comparisons,

Reference 32

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Observation a47e885e-bb73-45fa-aa2b-efb3ce777406 · outbound

This paper cites Accurate channel prediction based on transformer: Making mobility negligible,.

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge Accurate channel prediction based on transformer: Making mobility negligible,

Reference 33

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Observation d0f8327f-2207-4a96-b617-9fbc88efa357 · outbound

This paper cites Unsupervised learning strategy for direction-of-arrival estimation network,.

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge Unsupervised learning strategy for direction-of-arrival estimation network,

Reference 34

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Observation a9cc1a24-d6d3-4baa-b944-9f48111db976 · outbound

This paper cites UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Reference 35

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Observation 118cee2a-3ce7-426f-9300-511210925ee2 · outbound

This paper cites High-dimensional probability: An introduction with applications in data science,.

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge High-dimensional probability: An introduction with applications in data science,

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-03T13:09:40.864362Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T13:09:40.864362Z digest=sha256:d9348acde965da58440ac463ea43601c04ce261447757912d9920ad69ad206c6

Pith citing papers

Observation 4987e5c2-5dd5-4d3d-8cc5-4bd6b3ac90c1 · inbound

Federated Parameter-Efficient Adaptation for Interference Mitigation at the Wireless Edge cites this paper.

Federated Parameter-Efficient Adaptation for Interference Mitigation at the Wireless Edge CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-07-10T02:18:50.974760Z

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.

source=pdf_text observed=2026-05-10T08:10:49.967690Z digest=sha256:649552bcbcaa7eb27a2718b6f0f42adfa064779c9b053d10d5c5b4646e790131