Paper Citation Record · LEDGER

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training

As of 19 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 1 inbound Pith citation observation for arXiv:2505.12815.

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

pith.paper-citation-record.v1
2505.12815 v2

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:33:37.925834Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:16:14.798670Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T21:16:15.596911Z

Reference resolution

38 of 38 outbound references displayed

verified exact2
verified fuzzy35
unresolved1
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bb1a5747-7902-45e9-80ad-de028a7885e7 · outbound

This paper cites Federated learning for predicting clinical outcomes in patients with covid-19,.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training Federated learning for predicting clinical outcomes in patients with covid-19,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:39.050808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dd50128a-b417-4578-9018-e9c192fbff55 · outbound

This paper cites Federated learning for breast density classification: A real-world implementation,.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training Federated learning for breast density classification: A real-world implementation,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:38.994311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e39f0284-eff5-4c88-b58f-6aea57ad6014 · outbound

This paper cites Federated learning improves site performance in multicenter deep learning without data sharing,.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training Federated learning improves site performance in multicenter deep learning without data sharing,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:38.897655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a3184c00-78f7-44ad-906b-da48b378493e · outbound

This paper cites Evaluation of federated learning variations for covid-19 diagnosis using chest radiographs from 42 us and european hospitals,.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training Evaluation of federated learning variations for covid-19 diagnosis using chest radiographs from 42 us and european hospitals,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:38.887040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation fb757215-63c1-4712-9233-7f4c4576efde · outbound

This paper cites Federated learning enables big data for rare cancer boundary detection,.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training Federated learning enables big data for rare cancer boundary detection,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:38.876764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 84ca8b48-2eef-4ba3-8c95-60a3d27d44d8 · outbound

This paper cites Melloddy: Cross- pharma federated learning at unprecedented scale unlocks benefits in qsar without compromising proprietary information,.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training Melloddy: Cross- pharma federated learning at unprecedented scale unlocks benefits in qsar without compromising proprietary information,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:38.866138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ab256b27-bfb3-4467-bfd1-8e7c4b5c4c3b · outbound

This paper cites Fedpetuning: When federated learning meets the parameter-efficient tuning methods of pre-trained language models,.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training Fedpetuning: When federated learning meets the parameter-efficient tuning methods of pre-trained language models,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:38.855256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 76f961f7-2736-469f-b3cd-7646cae7b848 · outbound

This paper cites Federated fine-tuning of large language models under heterogeneous tasks and client resources,.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training Federated fine-tuning of large language models under heterogeneous tasks and client resources,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:38.841253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ee300473-4550-4af8-aedc-b52c96c2e64f · outbound

This paper cites Flora: Federated fine-tuning large language models with heterogeneous low-rank adaptations,.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training Flora: Federated fine-tuning large language models with heterogeneous low-rank adaptations,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:38.831709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5b642d1d-553b-4e79-b836-d6f5f141da2d · outbound

This paper cites Federated residual low-rank adaptation of large language models,.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training Federated residual low-rank adaptation of large language models,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:38.820931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ad18427e-56be-4c67-a3a4-e81feef6c52c · outbound

This paper cites Fedex-lora: Exact aggrega- tion for federated and efficient fine-tuning of large language models,.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training Fedex-lora: Exact aggrega- tion for federated and efficient fine-tuning of large language models,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:38.783241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 33ad88c6-0a08-4e5c-82c8-f2ce2139ba50 · outbound

This paper cites Improving lora in privacy-preserving federated learning,.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training Improving lora in privacy-preserving federated learning,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:38.622990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ab74ce4c-e418-49d0-9e33-f05a6525cc69 · outbound

This paper cites Unicron: Economizing Self-Healing LLM Training at Scale.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training Unicron: Economizing Self-Healing LLM Training at Scale

Reference 13

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Source-reported events for the cited work

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Observation 3a08cafc-3df1-4c5b-a1f2-5e5c1172d5ee · outbound

This paper cites Datastates-llm: Lazy asynchronous checkpointing for large language models,.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training Datastates-llm: Lazy asynchronous checkpointing for large language models,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:38.539534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a8fc9856-707a-4456-8545-06a713169783 · outbound

This paper cites Checkfreq: Frequent, fine-grained dnn checkpointing,.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training Checkfreq: Frequent, fine-grained dnn checkpointing,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:38.470657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 225a613b-7547-414a-bb5d-e2133ebae9d1 · outbound

This paper cites Just-in-time checkpointing: Low cost error recovery from deep learning training failures,.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training Just-in-time checkpointing: Low cost error recovery from deep learning training failures,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:38.460222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 188bca24-08c5-41ca-bfcc-3e43aa74d2a8 · outbound

This paper cites Check-n-run: A check- pointing system for training deep learning recommendation models,.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training Check-n-run: A check- pointing system for training deep learning recommendation models,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:38.450394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c7276eb5-cd0d-49fe-8bef-2ebbb8f59d15 · outbound

This paper cites Gemini: Fast failure recovery in distributed training with in-memory checkpoints,.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training Gemini: Fast failure recovery in distributed training with in-memory checkpoints,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:38.440994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 50c58506-781e-4b12-8fff-802b626f2a15 · outbound

This paper cites Pollux: Co-adaptive clus- ter scheduling for goodput-optimized deep learning,.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training Pollux: Co-adaptive clus- ter scheduling for goodput-optimized deep learning,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:38.431496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 73c5ee27-5fa4-4085-bb94-c8b89fceb1e1 · outbound

This paper cites Rubick: Exploiting Job Reconfigurability for Deep Learning Cluster Scheduling.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training Rubick: Exploiting Job Reconfigurability for Deep Learning Cluster Scheduling

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:33:37.973013Z

Source-reported events for the cited work

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Observation f2e16fa9-434d-4704-94dd-168520aac318 · outbound

This paper cites Optimus: an efficient dynamic resource scheduler for deep learning clusters,.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training Optimus: an efficient dynamic resource scheduler for deep learning clusters,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:38.421195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dd8094ff-9c74-4b64-8074-6502e00354bf · outbound

This paper cites Deepboot: Dynamic scheduling system for training and inference deep learning tasks in gpu cluster,.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training Deepboot: Dynamic scheduling system for training and inference deep learning tasks in gpu cluster,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:38.409460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cc693fd2-d382-43dc-9658-875cfdf69a43 · outbound

This paper cites Universal checkpointing: A flexible and efficient distributed checkpointing system for large-scale dnn training with reconfigurable parallelism,.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training Universal checkpointing: A flexible and efficient distributed checkpointing system for large-scale dnn training with reconfigurable parallelism,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:38.398690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a5433776-98d7-491f-8c17-624036ac160f · outbound

This paper cites Paddlepaddle: An open-source deep learning platform from industrial practice,.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training Paddlepaddle: An open-source deep learning platform from industrial practice,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:38.388263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 79b70c3d-3d72-4b91-9724-bf47b3af1e17 · outbound

This paper cites Elasticdl: A kubernetes-native deep learning framework with fault-tolerance and elastic scheduling,.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training Elasticdl: A kubernetes-native deep learning framework with fault-tolerance and elastic scheduling,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:38.363091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a38c8d34-5691-45a6-a8be-39bf093abafc · outbound

This paper cites Dl2: A deep learning-driven scheduler for deep learning clusters,.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training Dl2: A deep learning-driven scheduler for deep learning clusters,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:38.305471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6b11ba66-8093-407a-a292-5aa028b3e40a · outbound

This paper cites Elastic parameter server: Accelerating ml training with scalable resource scheduling,.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training Elastic parameter server: Accelerating ml training with scalable resource scheduling,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:38.255361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ae7f6daa-ad87-457c-8b07-d4d0349d2b97 · outbound

This paper cites Elastic deep learning in multi-tenant gpu clusters,.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training Elastic deep learning in multi-tenant gpu clusters,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:38.244563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 019a71cb-9e98-43fb-845b-2ffa93fb7a0d · outbound

This paper cites Elastic resource sharing for distributed deep learning,.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training Elastic resource sharing for distributed deep learning,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:38.234311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9ca9be8e-ef1a-47d1-9104-2fb17b682493 · outbound

This paper cites Elan: Towards generic and efficient elastic training for deep learning,.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training Elan: Towards generic and efficient elastic training for deep learning,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:38.223437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b971d182-f0d1-4e26-959d-8408d17a3d9b · outbound

This paper cites Resource elasticity in distributed deep learning,.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training Resource elasticity in distributed deep learning,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:38.213215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6f5478f8-0000-4ccf-99cd-f27d71ecc3bd · outbound

This paper cites Comparing decentralized learning to federated learning when training deep neural networks under churn,.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training Comparing decentralized learning to federated learning when training deep neural networks under churn,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:38.200771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation caa64a81-5a90-418a-baf6-b2e26d344caa · outbound

This paper cites Communication-Efficient Federated Learning under Dynamic Device Arrival and Departure: Convergence Analysis and Algorithm Design.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training Communication-Efficient Federated Learning under Dynamic Device Arrival and Departure: Convergence Analysis and Algorithm Design

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:33:37.958678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ce4b3a87-1b3a-4af4-accc-bb63d165e478 · outbound

This paper cites Mimic: Combating client dropouts in federated learning by mimicking central updates,.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training Mimic: Combating client dropouts in federated learning by mimicking central updates,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:38.133480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c2c13fb0-6c94-4780-a39c-13bb7e8257f3 · outbound

This paper cites Esync: Accelerating intra-domain federated learning in heterogeneous data centers,.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training Esync: Accelerating intra-domain federated learning in heterogeneous data centers,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:38.025439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d3a14cc4-d201-4277-b26a-1ba635be60e7 · outbound

This paper cites Accelerating geo-distributed ma- chine learning with network-aware adaptive tree and auxiliary route,.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training Accelerating geo-distributed ma- chine learning with network-aware adaptive tree and auxiliary route,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:38.015398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f9883d22-ef01-41b6-ab94-8cdb9aab66e1 · outbound

This paper cites Data heterogeneity-robust federated learning via group client selection in industrial iot,.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training Data heterogeneity-robust federated learning via group client selection in industrial iot,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:38.004206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6e8d9a27-fc0f-4bd5-805a-a013ffab0427 · outbound

This paper cites The art of computer programming,.

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training The art of computer programming,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:37.992980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

Observation 734e7039-4347-43df-8efd-e8282c1a0b65 · 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 Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training

Reference 77

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:16:15.600083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.