The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning | Paper Citation Record

Observation 1188c8c5-b763-4c10-b7de-1a607db3ff13 · outbound

This paper cites Federated dynamic sparse training: Computing less, communicating less, yet learning better.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Federated dynamic sparse training: Computing less, communicating less, yet learning better

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:45.772292Z

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Observation 4a88a252-556f-45e8-b1e8-9a659277f53f · outbound

This paper cites Large-scale machine learning with stochastic gradient descent.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Large-scale machine learning with stochastic gradient descent

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:45.509734Z

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Observation 71801e6f-0555-46f7-a5f0-cab9b514d8b6 · outbound

This paper cites Adaptive federated dropout: Improving communication efficiency and generalization for federated learning.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Adaptive federated dropout: Improving communication efficiency and generalization for federated learning

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:45.292484Z

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Observation 6ea8efb2-8aae-4f1a-bef3-7e629be6c175 · outbound

This paper cites Expanding the Reach of Federated Learning by Reducing Client Resource Requirements.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Expanding the Reach of Federated Learning by Reducing Client Resource Requirements

Reference 4

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Observation 816bba5b-0ec4-421a-b7e3-e65e05f762ec · outbound

This paper cites On generalization bounds of a family of recurrent neural networks.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning On generalization bounds of a family of recurrent neural networks

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:45.034728Z

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Observation b737cf8d-b92f-4bc7-b301-ba540da92a49 · outbound

This paper cites U., and Yurtsever, A.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning U., and Yurtsever, A

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:44.836204Z

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The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Unresolved cited work

Reference 7

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Observation f650a152-ea90-4b5b-9262-11002dcc29e9 · outbound

This paper cites S., Kobyzev, I., Nia, V.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning S., Kobyzev, I., Nia, V

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:44.445449Z

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Observation fd2fb8a4-00e2-4069-be15-eae0d09b551c · outbound

This paper cites Deep sparse rectifier neural networks.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Deep sparse rectifier neural networks

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:44.301401Z

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Observation c18d2636-5c05-4831-820c-d1af809e98a6 · outbound

This paper cites EF21-P and friends: Improved theoretical communication complexity for distributed optimization with bidirectional compression.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning EF21-P and friends: Improved theoretical communication complexity for distributed optimization with bidirectional compression

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:44.237923Z

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Observation 2e46e3ab-047e-473d-8cf3-4a5f93f60f7d · outbound

This paper cites Lora+: Efficient low rank adaptation of large models.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Lora+: Efficient low rank adaptation of large models

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:44.142010Z

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Observation f9842699-dab3-4361-bb08-daa6912e9724 · outbound

This paper cites Deep residual learning for image recognition.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Deep residual learning for image recognition

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:44.048949Z

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Observation 54be768d-91fe-47be-8d2c-6c767d86d5ee · outbound

This paper cites an unresolved cited work.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Unresolved cited work

Reference 13

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Observation c6703888-407a-48f9-ace9-e9c785fabafe · outbound

This paper cites J., Shen, Y., Wallis, P., Allen - Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning J., Shen, Y., Wallis, P., Allen - Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:43.879021Z

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Observation 52242101-0396-4b5b-8623-9fd8ea70e20a · outbound

This paper cites Fedpara: Low-rank hadamard product for communication-efficient federated learning.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Fedpara: Low-rank hadamard product for communication-efficient federated learning

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:43.767509Z

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Observation 202486ce-743a-4698-986b-d9232541aeca · outbound

This paper cites and Szegedy, C.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning and Szegedy, C

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:43.689110Z

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Observation 6af0a4f9-b8b2-44e0-b7c2-da02d795bf52 · outbound

This paper cites Sparse random networks for communication-efficient federated learning.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Sparse random networks for communication-efficient federated learning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:43.605644Z

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Observation 171376e3-6209-4a52-91d4-fa5292fae125 · outbound

This paper cites P., Rebjock, Q., Stich, S.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning P., Rebjock, Q., Stich, S

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:43.521214Z

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Observation 1dec8dfc-8e8d-480c-8128-476d3760928a · outbound

This paper cites B., Yu, F.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning B., Yu, F

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:43.444411Z

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Observation 26233bbc-2415-491a-b4d5-dadb96fb0094 · outbound

This paper cites and Hinton, G.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning and Hinton, G

Reference 20

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This paper cites and Yang, X.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning and Yang, X

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:43.310044Z

Source-reported events for the cited work

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Observation 6883764d-1fb3-435b-a819-9c071177a14f · outbound

This paper cites Federated learning on non-iid data silos: An experimental study.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Federated learning on non-iid data silos: An experimental study

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:43.190963Z

Source-reported events for the cited work

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Observation 2d6f5c57-928b-4c2d-bf37-be618556f222 · outbound

This paper cites Adaptive low-precision training for embeddings in click-through rate prediction.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Adaptive low-precision training for embeddings in click-through rate prediction

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:43.086387Z

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Observation 6cbc252b-063b-473c-a294-b9df7cb2de0a · outbound

This paper cites Masked random noise for communication-efficient federated learning.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Masked random noise for communication-efficient federated learning

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:42.895608Z

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Observation 760bec13-9d11-4543-abe8-cceeea124d36 · outbound

This paper cites Embedding compression in recommender systems: A survey.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Embedding compression in recommender systems: A survey

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:42.711327Z

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Observation ae1d083a-d426-42f9-8210-e2f1c1b9a29e · outbound

This paper cites Mixed-Precision Embeddings for Large-Scale Recommendation Models.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Mixed-Precision Embeddings for Large-Scale Recommendation Models

Reference 26

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Observation 56ca60dd-fadc-48e5-8fc3-7f5593ca9e0d · outbound

This paper cites Fedbat: Communication-efficient federated learning via learnable binarization.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Fedbat: Communication-efficient federated learning via learnable binarization

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:42.464994Z

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Observation 164dac78-20e0-4dc6-b52b-f8d15ba19759 · outbound

This paper cites Beyond zero initialization: Investigating the impact of non-zero initialization on lora fine-tuning dynamics.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Beyond zero initialization: Investigating the impact of non-zero initialization on lora fine-tuning dynamics

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:42.249110Z

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Observation f4495d22-a5e1-4955-be49-63d38c9b9839 · outbound

This paper cites Bora: Towards more expressive low-rank adaptation with block diversity.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Bora: Towards more expressive low-rank adaptation with block diversity

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:42.045018Z

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Observation f1069f33-8c00-4c96-8806-4c0f28739c3e · outbound

This paper cites On the convergence of fedavg on non-iid data.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning On the convergence of fedavg on non-iid data

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:41.820805Z

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Observation 89eb1d25-a938-4ab2-8cf0-889f704c798b · outbound

This paper cites Relora: High-rank training through low-rank updates.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Relora: High-rank training through low-rank updates

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:41.608030Z

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Observation 54cf3391-739f-4733-81c5-5c5189dce5e4 · outbound

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The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Unresolved cited work

Reference 32

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Observation f172aa8f-473b-411d-a462-fb8c5f950b7b · outbound

This paper cites Mgr: Multi-generator based rationalization.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Mgr: Multi-generator based rationalization

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:41.224114Z

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Observation 03d2d073-b658-4401-aa40-0e44d25f74de · outbound

This paper cites D-separation for causal self-explanation.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning D-separation for causal self-explanation

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:41.064801Z

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Observation 5c4995bb-90ef-45f9-b0d4-54970e50391b · outbound

This paper cites Is the mmi criterion necessary for interpretability? degenerating non-causal features to plain noise for self-rationalization.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Is the mmi criterion necessary for interpretability? degenerating non-causal features to plain noise for self-rationalization

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:40.822753Z

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Observation 5fd880f8-0c1a-4b12-ab9b-7726069066aa · outbound

This paper cites Enhancing the rationale-input alignment for self-explaining rationalization.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Enhancing the rationale-input alignment for self-explaining rationalization

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:40.631288Z

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Observation 636612de-5b93-416f-8b75-67517c4d7362 · outbound

This paper cites Exploring practical gaps in using cross entropy to implement maximum mutual information criterion for rationalization.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Exploring practical gaps in using cross entropy to implement maximum mutual information criterion for rationalization

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:40.470304Z

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Observation 12c7166d-abc1-4c7d-8d55-66daee43ab7a · outbound

This paper cites Breaking free from mmi: A new frontier in rationalization by probing input utilization.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Breaking free from mmi: A new frontier in rationalization by probing input utilization

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:40.277001Z

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Observation eadd02ec-4659-47fd-b78e-7f06d9f92563 · outbound

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The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Unresolved cited work

Reference 39

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Observation 78df04c9-5c7a-4ae2-be4a-118edeae1f2a · outbound

This paper cites A survey on lora of large language models.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning A survey on lora of large language models

Reference 40

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The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Unresolved cited work

Reference 41

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This paper cites Resource-adaptive federated learning with all-in-one neural composition.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Resource-adaptive federated learning with all-in-one neural composition

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:39.709670Z

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Observation 4a6684fc-5c96-463e-ae3d-c1472f60fbcb · outbound

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The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Unresolved cited work

Reference 43

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Observation ab874529-1b66-4643-a80c-405f298911d3 · outbound

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The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Unresolved cited work

Reference 44

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:59:39.365008Z

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Observation e3a4b659-69a4-4223-8993-d5b399a7c516 · outbound

This paper cites an unresolved cited work.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Unresolved cited work

Reference 45

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:59:39.162347Z

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Observation 53965c88-111c-4367-934d-77aa6b9bea32 · outbound

This paper cites Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:38.955301Z

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Observation 6320fa69-3b1b-414b-8b9e-babd56d7315a · outbound

This paper cites N., Kingsbury, B., Sindhwani, V., Arisoy, E., and Ramabhadran, B.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning N., Kingsbury, B., Sindhwani, V., Arisoy, E., and Ramabhadran, B

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:38.777748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f40d9880-b9d4-4a07-9f28-d6b6df000205 · outbound

This paper cites an unresolved cited work.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Unresolved cited work

Reference 48

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:59:38.583347Z

Source-reported events for the cited work

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Observation 5f78b847-c383-4495-ba2d-a7fc47ee0aa7 · outbound

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

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Improving lora in privacy-preserving federated learning

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:38.337272Z

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Observation e9d2fc8d-1dc0-4f0a-953d-b3115dd59888 · outbound

This paper cites Doublesqueeze: Parallel stochastic gradient descent with double-pass error-compensated compression.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Doublesqueeze: Parallel stochastic gradient descent with double-pass error-compensated compression

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:38.225687Z

Source-reported events for the cited work

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Observation 71328d49-fcf3-43b5-94d1-450f2d98b816 · outbound

This paper cites Decoupling general and personalized knowledge in federated learning via additive and low-rank decomposition.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Decoupling general and personalized knowledge in federated learning via additive and low-rank decomposition

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:38.096713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 949187dc-3082-4caa-87f3-6702317c7aa9 · outbound

This paper cites Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms

Reference 52

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Observation 52c28638-7dc0-45cd-bb60-97f530705ea4 · outbound

This paper cites Communication-efficient federated learning with binary neural networks.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Communication-efficient federated learning with binary neural networks

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:37.971708Z

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Observation b9cb03b2-28c5-442e-86e0-4d0e6a8d0bc6 · outbound

This paper cites Fedhm: Efficient federated learning for heterogeneous models via low-rank factorization.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Fedhm: Efficient federated learning for heterogeneous models via low-rank factorization

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:37.854603Z

Source-reported events for the cited work

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Observation b605ee28-ee8f-428d-a9e6-fc553183e39d · outbound

This paper cites Parallel restarted SGD with faster convergence and less communication: Demystifying why model averaging works for deep learning.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning Parallel restarted SGD with faster convergence and less communication: Demystifying why model averaging works for deep learning

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:37.679532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 58132511-13ed-436d-90a9-36de9f1e2e19 · outbound

This paper cites H., Hoang, T.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning H., Hoang, T

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:37.511730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 473a39c1-9cc9-48b4-975b-2bae6fa9c986 · outbound

This paper cites M., and Gu, Q.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning M., and Gu, Q

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:37.288634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9eadfe3e-cbe1-480b-a4d4-2d1cb3e591b9 · outbound

This paper cites write newline.

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning write newline

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T12:59:36.950520Z

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