Paper Citation Record · LEDGER

FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting

As of 23 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2608.01290.

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

pith.paper-citation-record.v1
2608.01290 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T15:11:57.638536Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

33 of 33 outbound references displayed

verified exact2
verified fuzzy15
unresolved16
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8f9cfe55-9e45-4c55-aabb-13b4a92a98f5 · outbound

This paper cites AI-based market intelligence systems for farmer collectives: A case study from India,.

FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting AI-based market intelligence systems for farmer collectives: A case study from India,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:11:58.526065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f0c3a59f-b4b3-440a-81a4-68bcaae46303 · outbound

This paper cites The Digital Personal Data Protection Act, 2023,.

FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting The Digital Personal Data Protection Act, 2023,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:11:58.505624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d947bdc3-9d46-4235-87dd-ca7212a57a42 · outbound

This paper cites Regulation (EU) 2016/679 — General Data Protection Regulation,.

FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting Regulation (EU) 2016/679 — General Data Protection Regulation,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:11:58.485650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c084a414-9528-4fcc-a325-6b6268cd796c · outbound

This paper cites Chronos: Learning the Language of Time Series.

FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting Chronos: Learning the Language of Time Series

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T15:11:57.449099Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 119b5508-d9ed-4623-9b00-3d0bde9d857d · outbound

This paper cites Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting.

FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T15:11:57.455502Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72e83048-7313-473d-8efa-152d64c59214 · outbound

This paper cites A decoder-only foundation model for time-series forecasting.

FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting A decoder-only foundation model for time-series forecasting

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T15:11:57.461129Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 433ac464-e856-4329-9e4d-5a986bc5e5e9 · outbound

This paper cites TimeGPT-1.

FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting TimeGPT-1

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T15:11:57.468156Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83a846b7-3f70-4b63-9ce1-cf3fc853bd45 · outbound

This paper cites Attention is all you need,.

FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting Attention is all you need,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T15:11:57.475952Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 983a49cb-f264-49a5-bf80-0c55dab8deb5 · outbound

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

FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting Communication-efficient learning of deep networks from decentralized data,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:11:58.438457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1393485c-606d-407a-86c9-1aaa72ffa19f · outbound

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

FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting LoRA: Low-rank adaptation of large language models,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T15:11:57.487195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b5b7ac4-5152-4e11-af6b-77eee6eb8c48 · outbound

This paper cites Federated adaptive fine-tuning of large language models with heterogeneous quantization and LoRA,.

FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting Federated adaptive fine-tuning of large language models with heterogeneous quantization and LoRA,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:11:58.401469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 833e9990-6686-4ace-b261-0be1e88ea40f · outbound

This paper cites A survey on federated fine-tuning of large language models,.

FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting A survey on federated fine-tuning of large language models,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T15:11:57.497604Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b8fbee4-102e-411e-ac9d-77ce023851cd · outbound

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

FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting Exploring the limits of transfer learning with a unified text-to-text transformer,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T15:11:57.504513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4eefeece-13c1-4393-a4df-f554ce475dc0 · outbound

This paper cites Federated optimization in heterogeneous networks,.

FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting Federated optimization in heterogeneous networks,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:11:58.366790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b13b98dd-8dae-4c39-8980-23a9959bbcd1 · outbound

This paper cites Deep learning with differential privacy,.

FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting Deep learning with differential privacy,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:11:58.349222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation dfe48784-3523-4c93-9ace-3cbd04a12f91 · outbound

This paper cites Federated foundation models on heterogeneous time series,.

FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting Federated foundation models on heterogeneous time series,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:11:58.332034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2c3439de-d315-48aa-a7f0-19a6cf5f6c89 · outbound

This paper cites Discrete Prototypical Memories for Federated Time Series Foundation Models.

FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting Discrete Prototypical Memories for Federated Time Series Foundation Models

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-15T15:11:57.910771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 56a4a406-23d8-4e1e-946f-1ff4a268d362 · outbound

This paper cites Time-LLM: Time series forecasting by reprogramming large language models,.

FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting Time-LLM: Time series forecasting by reprogramming large language models,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T15:11:57.531884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17415ca8-2df9-417a-8e21-ffed39fb6330 · outbound

This paper cites Federated Learning with Non-IID Data.

FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting Federated Learning with Non-IID Data

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T15:11:57.536883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c400ae42-4c2e-42be-be8f-0e8d2b8a66c8 · outbound

This paper cites QLoRA: Efficient finetuning of quantized language models,.

FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting QLoRA: Efficient finetuning of quantized language models,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T15:11:57.542674Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e062616f-ceda-47a0-ba6a-8689cefe89a6 · outbound

This paper cites Edge-FIT: Federated instruction tuning of quantized LLMs for privacy-preserving smart home environments,.

FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting Edge-FIT: Federated instruction tuning of quantized LLMs for privacy-preserving smart home environments,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:11:58.287350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation eee4b43a-6ec7-424b-b604-bbbf567ba133 · outbound

This paper cites Agricultural data privacy and federated learning: A review of challenges and opportuni- ties,.

FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting Agricultural data privacy and federated learning: A review of challenges and opportuni- ties,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:11:58.270165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 45297fc7-2f67-4b56-aada-9d5643a5d322 · outbound

This paper cites Federated learning-based approach for crop recommendation and market stability in agriculture,.

FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting Federated learning-based approach for crop recommendation and market stability in agriculture,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:11:58.254373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 12e5f8d8-92f3-4a17-82f2-c141ac7a66ca · outbound

This paper cites VLLFL: A vision-language model based lightweight federated learning framework for smart agriculture,.

FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting VLLFL: A vision-language model based lightweight federated learning framework for smart agriculture,

Reference 24

Resolution: verified exact; raw_fallback, observed 2026-08-15T15:11:57.839285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4036cb69-68e2-4f39-b324-f762f1eaa27e · outbound

This paper cites AgriGen: A prompt-tuned, mul- tilingual LLM-based Q&A system for smarter agriculture,.

FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting AgriGen: A prompt-tuned, mul- tilingual LLM-based Q&A system for smarter agriculture,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:11:58.234670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d328bb4e-816b-4078-9b1e-305dcc6c0f04 · outbound

This paper cites A review paper on the study of deep learning and machine learning models used in forecasting Indian crop prices,.

FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting A review paper on the study of deep learning and machine learning models used in forecasting Indian crop prices,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:11:58.212299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5a65a52c-c70e-48a0-aa4d-d1a57212861e · outbound

This paper cites Flower: A Friendly Federated Learning Research Framework.

FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting Flower: A Friendly Federated Learning Research Framework

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T15:11:57.591768Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4d8f61e-e4f4-473a-91fb-323ba8ab8c14 · outbound

This paper cites The algorithmic foundations of differential privacy,.

FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting The algorithmic foundations of differential privacy,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T15:11:57.603784Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2af4c971-d0eb-439d-848f-e5ad98bb3f74 · outbound

This paper cites R ´enyi differential privacy,.

FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting R ´enyi differential privacy,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:11:58.179523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c3fbbc64-6a2f-4a07-a58c-7704da91ba3d · outbound

This paper cites an unresolved cited work.

FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting Unresolved cited work

Reference 30

Resolution: unresolved; raw_fallback, observed 2026-08-15T15:11:58.160936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2b393da2-c414-4b49-b50a-4aa6ac557299 · outbound

This paper cites Long short-term memory,.

FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting Long short-term memory,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T15:11:57.625539Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1681474-b917-419c-9163-ecd424c23a57 · outbound

This paper cites Adding Gradient Noise Improves Learning for Very Deep Networks.

FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting Adding Gradient Noise Improves Learning for Very Deep Networks

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-15T15:11:57.632296Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1dc2e425-4e12-455e-bd12-62869cc3206d · outbound

This paper cites Generalization in adaptive data analysis and holdout reuse,.

FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting Generalization in adaptive data analysis and holdout reuse,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:11:58.126838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

No inbound Pith citation observations are available.