DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data | Paper Citation Record

Observation edb4f29f-3fc3-4d5a-8514-37e4bd31f312 · outbound

This paper cites GPT-4 Technical Report.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data GPT-4 Technical Report

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Observation 499944fe-89b2-4f51-8c26-11adc3af3a8c · outbound

This paper cites Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

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This paper cites Aketi, A.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Aketi, A

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This paper cites Greedy Layerwise Learning Can Scale To ImageNet.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Greedy Layerwise Learning Can Scale To ImageNet

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This paper cites Greedy Layer-Wise Training of Deep Networks.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Greedy Layer-Wise Training of Deep Networks

Reference 5

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Observation 93c9543e-03e0-40e3-b834-b7f145f06a7f · outbound

This paper cites Boyd, Arpita Ghosh, Balaji Prabhakar, and Devavrat Shah.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Boyd, Arpita Ghosh, Balaji Prabhakar, and Devavrat Shah

Reference 6

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This paper cites On the Importance and Applicability of Pre-Training for Federated Learning.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data On the Importance and Applicability of Pre-Training for Federated Learning

Reference 7

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DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Unresolved cited work

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Observation 93f5c017-941c-4746-bcba-144a9f781e2a · outbound

This paper cites Training Deep Nets with Sublinear Memory Cost.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Training Deep Nets with Sublinear Memory Cost

Reference 9

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Observation 6afc004f-ad51-41f0-a026-a7968ce858a7 · outbound

This paper cites Chiang, Z.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Chiang, Z

Reference 10

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Observation d5c00897-7d43-48c3-b485-40a5c0ac450b · outbound

This paper cites Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models

Reference 11

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Observation 4797dc6a-ee3a-4a7f-a48d-c1a9a8215204 · outbound

This paper cites Epidemic Learning: Boosting Decentralized Learning with Randomized Communication.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Epidemic Learning: Boosting Decentralized Learning with Randomized Communication

Reference 12

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Observation 99422e78-2985-4668-9368-f4a06b634015 · outbound

This paper cites QLoRA: Efficient Finetuning of Quantized LLMs.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data QLoRA: Efficient Finetuning of Quantized LLMs

Reference 13

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Observation 096aa649-2f75-4af4-b16d-41a079ee9135 · outbound

This paper cites The Llama 3 Herd of Models.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data The Llama 3 Herd of Models

Reference 14

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Observation 6967a7cb-5021-4109-997d-55763777e53e · outbound

This paper cites Cross-Gradient Aggregation for Decentralized Learning from Non-IID Data.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Cross-Gradient Aggregation for Decentralized Learning from Non-IID Data

Reference 15

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Observation f56364f2-8f27-4b5b-a9e7-24a9126809f0 · outbound

This paper cites Decentralized low-rank fine- tuning of large language models.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Decentralized low-rank fine- tuning of large language models

Reference 16

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Observation 925890e9-16e6-4a66-8cda-a790efedce2d · outbound

This paper cites Robust Decentralized Learning With Local Updates and Gradient Tracking.IEEE Transactions on Networking, 33(4):2036–2048, 2025.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Robust Decentralized Learning With Local Updates and Gradient Tracking.IEEE Transactions on Networking, 33(4):2036–2048, 2025

Reference 17

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Observation 800e4c2f-83b0-4e88-a615-601a0b8dc824 · outbound

This paper cites Selective Aggregation for Low-Rank Adaptation in Federated Learning.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Selective Aggregation for Low-Rank Adaptation in Federated Learning

Reference 18

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Observation 6e32fde0-5fb4-4878-a899-0024c0de3d9f · outbound

This paper cites Parameter-Efficient Transfer Learning for NLP.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Parameter-Efficient Transfer Learning for NLP

Reference 19

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DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Unresolved cited work

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Observation 35b3e6bd-360d-4959-b44b-062795d8e287 · outbound

This paper cites Kingma and Jimmy Ba.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Kingma and Jimmy Ba

Reference 21

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Observation c47f0dbe-2f79-4864-ac73-5e82eeb64ae4 · outbound

This paper cites NOLA: Compressing LoRA using Linear Combination of Random Basis.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data NOLA: Compressing LoRA using Linear Combination of Random Basis

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-07-02T01:56:28.063989Z

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Observation c63b60f6-376f-46dd-9dac-55960b958d0d · outbound

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DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Unresolved cited work

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Observation 81c553a7-60b3-4e46-805b-39df7d2ea10c · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 24

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DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Unresolved cited work

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Observation 6aaafc06-c130-4f0d-ad58-bf2f4a7507e8 · outbound

This paper cites Measuring the Intrinsic Dimension of Objective Landscapes.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Measuring the Intrinsic Dimension of Objective Landscapes

Reference 26

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Observation 0215f415-f062-4123-880b-44cfaab6b01c · outbound

This paper cites Prefix-Tuning: Optimizing Continuous Prompts for Generation.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 27

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Observation db2a7a79-8dd5-4b4c-b8ef-bdfac76117c8 · outbound

This paper cites ReLoRA: High-Rank Training Through Low-Rank Updates.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data ReLoRA: High-Rank Training Through Low-Rank Updates

Reference 28

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Observation cae1bb0a-eb37-4725-bf3f-ef44bcb2c44a · outbound

This paper cites Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent

Reference 29

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Observation f72ed552-06e7-40bb-a1ee-b50333309b07 · outbound

This paper cites Stich, and Martin Jaggi.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Stich, and Martin Jaggi

Reference 30

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This paper cites HiFT: A Hierarchical Full Parameter Fine-Tuning Strategy.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data HiFT: A Hierarchical Full Parameter Fine-Tuning Strategy

Reference 31

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Observation 96d8b2f5-9138-4aa3-847e-d2dff7ba3e81 · outbound

This paper cites Lu and L.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Lu and L

Reference 32

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DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Unresolved cited work

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Observation eddbb560-413c-489c-9b7d-15d15061085c · outbound

This paper cites Full Parameter Fine-tuning for Large Language Models with Limited Resources.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Full Parameter Fine-tuning for Large Language Models with Limited Resources

Reference 34

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Observation b3751456-ad08-43b7-b6a5-c3de41305498 · outbound

This paper cites Lee, Danqi Chen, and Sanjeev Arora.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Lee, Danqi Chen, and Sanjeev Arora

Reference 35

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Observation 5ebc4753-c534-4a31-9eb5-dfe55832fe91 · outbound

This paper cites Communication-Efficient Learning of Deep Networks from Decentralized Data.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Communication-Efficient Learning of Deep Networks from Decentralized Data

Reference 36

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DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Unresolved cited work

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Observation 0702b669-d931-47a0-b526-7cb75794843a · outbound

This paper cites Empirical Analysis of the Strengths and Weaknesses of PEFT Techniques for LLMs.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Empirical Analysis of the Strengths and Weaknesses of PEFT Techniques for LLMs

Reference 38

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Observation 243030a8-e3f5-4ee5-858d-ada832ed4220 · outbound

This paper cites Pu and A.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Pu and A

Reference 39

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Observation 35b1f831-9c27-400e-bf4e-8eb7c0ec1ce7 · outbound

This paper cites FDLoRA: Personalized Federated Learning of Large Language Model via Dual LoRA Tuning.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data FDLoRA: Personalized Federated Learning of Large Language Model via Dual LoRA Tuning

Reference 40

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Observation 5718fe5e-6929-42f4-a5c3-d7f8a66a4f65 · outbound

This paper cites Federated full-parameter tuning of billion-sized language models with communication cost under 18 kilobytes.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Federated full-parameter tuning of billion-sized language models with communication cost under 18 kilobytes

Reference 41

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Observation ecac5325-29dd-4201-aec0-a8e3155242ee · outbound

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DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Unresolved cited work

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Observation b2d4cd61-3c3b-4a90-aa2b-61a77e37f6bd · outbound

This paper cites ZeRO-Offload: Democratizing Billion-Scale Model Training.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data ZeRO-Offload: Democratizing Billion-Scale Model Training

Reference 43

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This paper cites Waite, Shreyan Ganguly, Aditya Balu, Chinmay Hegde, and Soumik Sarkar.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Waite, Shreyan Ganguly, Aditya Balu, Chinmay Hegde, and Soumik Sarkar

Reference 44

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Observation 8b9a892e-82d0-4695-b589-b9ab565b49d2 · outbound

This paper cites Scaman, F.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Scaman, F

Reference 45

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Observation b561d55e-ef1a-410b-94fe-9466a5a48b99 · outbound

This paper cites Scaman, F.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Scaman, F

Reference 46

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Observation 6b0f07c4-042a-493f-ad6f-77fd3d2d4e54 · outbound

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DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Unresolved cited work

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Observation 45fbaf60-721b-4091-8115-3ff65399e6b7 · outbound

This paper cites Ferret: Federated full-parameter tuning at scale for large language models.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Ferret: Federated full-parameter tuning at scale for large language models

Reference 48

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Observation 1757ec6f-8f0d-4e06-903b-9bf88f9d5b5d · outbound

This paper cites Improving LoRA in Privacy-preserving Federated Learning.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Improving LoRA in Privacy-preserving Federated Learning

Reference 49

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Observation 4fe63e29-8975-445d-8861-38d7ed692cad · outbound

This paper cites Takezawa, H.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Takezawa, H

Reference 50

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Observation 901a72aa-8946-4625-95a2-9ffec0e06775 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-07-02T01:56:28.071202Z

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Observation c5095d82-4530-4e6a-906b-99885b440ca8 · outbound

This paper cites Convergence of A Block Coordinate Descent Method for Nondifferentiable Minimization.Journal of Optimization Theory and Applications, 109(3):475–494, 2001.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Convergence of A Block Coordinate Descent Method for Nondifferentiable Minimization.Journal of Optimization Theory and Applications, 109(3):475–494, 2001

Reference 52

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Observation 56bcd9f1-6013-4239-bcb7-c9313cdd84ca · outbound

This paper cites Vaswani, N.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Vaswani, N

Reference 53

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Observation e1ef07ac-90b4-4d1d-9b89-91044e1fe4dc · outbound

This paper cites ROSS: RObust decentralized Stochastic learning based on Shapley values.IEEE Transactions on Networking, 34:2911–2926, 2026.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data ROSS: RObust decentralized Stochastic learning based on Shapley values.IEEE Transactions on Networking, 34:2911–2926, 2026

Reference 54

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Observation 703fbc64-efc2-4597-9afd-c9c092642795 · outbound

This paper cites PDSL: Privacy-Preserved Decen- tralized Stochastic Learning with Heterogeneous Data Distribution.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data PDSL: Privacy-Preserved Decen- tralized Stochastic Learning with Heterogeneous Data Distribution

Reference 55

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Observation 7e351643-4507-4a5e-bf71-f796140208ee · outbound

This paper cites FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations

Reference 56

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Observation 2279315b-b694-40cb-9ef8-ce8c9784af46 · outbound

This paper cites Flexora: Flexible Low-Rank Adaptation for Large Language Models.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Flexora: Flexible Low-Rank Adaptation for Large Language Models

Reference 57

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Observation 5ffd6e22-83d8-4096-a0dd-8f142c183f5b · outbound

This paper cites Lawrie, and Benjamin Van Durme.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Lawrie, and Benjamin Van Durme

Reference 58

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Observation 41c7ae74-6d09-461d-bb8f-6d8d458a92f4 · outbound

This paper cites Coordinate Descent Algorithms.Mathematical Programming, 151(1):3–34, 2015.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Coordinate Descent Algorithms.Mathematical Programming, 151(1):3–34, 2015

Reference 59

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Observation f9e8e486-ad04-4021-bd09-540f1ee3dc7a · outbound

This paper cites Chain of LoRA: Efficient Fine-tuning of Language Models via Residual Learning.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Chain of LoRA: Efficient Fine-tuning of Language Models via Residual Learning

Reference 60

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Observation 46c1c874-9ad8-4735-b691-e0f5ffad9904 · outbound

This paper cites an unresolved cited work.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Unresolved cited work

Reference 61

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Observation 6c1989f7-e407-4890-95df-51150072719f · outbound

This paper cites Qwen2 Technical Report.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Qwen2 Technical Report

Reference 62

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Observation eb55767a-bf5e-4040-a08d-61906581f9e4 · outbound

This paper cites Qwen2.5 Technical Report.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Qwen2.5 Technical Report

Reference 63

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Observation 030de760-fecf-43e4-a1e9-2648233bcaff · outbound

This paper cites On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization

Reference 64

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Observation f670caea-572b-4e04-ad2a-7941e58fb3e0 · outbound

This paper cites When Scaling Meets LLM Fine- tuning: The Effect of Data, Model and Finetuning Method.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data When Scaling Meets LLM Fine- tuning: The Effect of Data, Model and Finetuning Method

Reference 65

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Observation cd70d297-c054-4ca4-8849-2c017704d3e5 · outbound

This paper cites LoRA-FA: Efficient and Effective Low Rank Representation Fine-tuning.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data LoRA-FA: Efficient and Effective Low Rank Representation Fine-tuning

Reference 66

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Observation ebd3aef3-1b68-4171-8ee7-49aebeaab99e · outbound

This paper cites Zhang, X.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Zhang, X

Reference 67

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Observation 2ddb099d-ce0f-4474-91f4-c54bca9053d4 · outbound

This paper cites NET- FLEET: Achieving Linear Convergence Speedup for Fully Decentralized Federated Learning with Heterogeneous Data.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data NET- FLEET: Achieving Linear Convergence Speedup for Fully Decentralized Federated Learning with Heterogeneous Data

Reference 68

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Observation 5b201741-d27c-44ce-a82d-15ab76f03edb · outbound

This paper cites Kingma, Yinyu Ye, Zhi-Quan Luo, and Ruoyu Sun.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Kingma, Yinyu Ye, Zhi-Quan Luo, and Ruoyu Sun

Reference 69

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Observation 89af02eb-2b78-484f-80da-9131fdae1f1c · outbound

This paper cites Enhancing Storage and Computational Efficiency in Federated Multimodal Learning for Large-Scale Models.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Enhancing Storage and Computational Efficiency in Federated Multimodal Learning for Large-Scale Models

Reference 70

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Observation c20a25dc-5121-498b-b0be-af3b72b11b0b · outbound

This paper cites FedPrompt: Communication- Efficient and Privacy-Preserving Prompt Tuning in Federated Learning.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data FedPrompt: Communication- Efficient and Privacy-Preserving Prompt Tuning in Federated Learning

Reference 71

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Observation eeae4342-fe8a-4f76-b143-c84633c397ef · outbound

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

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Galore: Memory-efficient LLM training by gradient low-rank projection

Reference 72

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Observation 04051c6e-c5e4-4a91-accf-bb33f2d7a60c · outbound

This paper cites Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena

Reference 73

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Observation be66033f-9cd3-494f-939d-b578cd2fa4eb · outbound

This paper cites Bullish” (positive) or “Bearish.

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data Bullish” (positive) or “Bearish

Reference 74

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