Label Privacy in Split Learning for Large Models with Parameter-Efficient Training | Paper Citation Record

Observation 078f6f19-4c0d-47a4-b195-be03f7a020d3 · outbound

This paper cites Practical secure aggregation for privacy-preserving machine learning.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training Practical secure aggregation for privacy-preserving machine learning

Reference 1

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Observation 3582bcaa-18b2-435a-bc51-fe6dea68a540 · outbound

This paper cites Petals: Collaborative Inference and Fine-tuning of Large Models.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training Petals: Collaborative Inference and Fine-tuning of Large Models

Reference 2

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Observation 3b4ce599-d1b3-4721-8448-53df3a3476f0 · outbound

This paper cites XGBoost : A scalable tree boosting system.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training XGBoost : A scalable tree boosting system

Reference 3

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Observation 4a1e1c1f-3de2-4af6-84c8-66c32e9d4b68 · outbound

This paper cites Scaling Instruction-Finetuned Language Models.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training Scaling Instruction-Finetuned Language Models

Reference 4

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Observation 76b6093c-b6b6-416e-9b47-f98d68dd7db4 · outbound

This paper cites QLoRA: Efficient Finetuning of Quantized LLMs.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training QLoRA: Efficient Finetuning of Quantized LLMs

Reference 5

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Observation 6bee8dd3-9201-412e-996e-c9a6168183b8 · outbound

This paper cites D reambooth A P I – E asily finetune S table D iffusion and generate customised A I images --- dreamboothapi.ai.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training D reambooth A P I – E asily finetune S table D iffusion and generate customised A I images --- dreamboothapi.ai

Reference 6

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Observation 8f681012-46c6-43b4-be22-5d07bb092aa5 · outbound

This paper cites Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models

Reference 7

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Observation 6022b683-7d75-4326-b46b-db67e28b3c14 · outbound

This paper cites Differential privacy.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training Differential privacy

Reference 8

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Observation fdc8151d-0e29-4b7a-9992-07b05d7eedfd · outbound

This paper cites Unsupervised domain adaptation by backpropagation.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training Unsupervised domain adaptation by backpropagation

Reference 9

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Observation 966c983a-4222-4108-8318-957ebe19e6e8 · outbound

This paper cites Distributed learning of deep neural network over multiple agents.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training Distributed learning of deep neural network over multiple agents

Reference 10

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Observation 28606261-f6fc-4031-a2d4-86d8b74d6685 · outbound

This paper cites WARP : W ord-level A dversarial R e P rogramming.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training WARP : W ord-level A dversarial R e P rogramming

Reference 11

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Observation a9d227a4-9aca-4115-8c2c-abf7ceb540f9 · outbound

This paper cites Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption, 2017.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption, 2017

Reference 12

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Observation 275e4c23-3461-44eb-9523-d736574b10e5 · outbound

This paper cites Deberta: Decoding-enhanced bert with disentangled attention.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training Deberta: Decoding-enhanced bert with disentangled attention

Reference 13

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Observation e57e1296-344d-4e5d-8137-5e0ec1ed8a96 · outbound

This paper cites Parameter-efficient transfer learning for NLP.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training Parameter-efficient transfer learning for NLP

Reference 14

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Observation fd15cdb1-4d56-4809-95ad-c124dc6105a4 · outbound

This paper cites Lo RA : Low-rank adaptation of large language models.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training Lo RA : Low-rank adaptation of large language models

Reference 15

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Observation b465f6ec-9bbf-43e2-8b78-75145359228d · outbound

This paper cites A uto T rain --- huggingface.co.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training A uto T rain --- huggingface.co

Reference 16

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Observation dc3698c8-8b68-48f8-8b80-a5051292eb37 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training Adam: A Method for Stochastic Optimization

Reference 17

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Observation 2cd7ae6f-7aec-402b-ad39-ec36f9682379 · outbound

This paper cites Label leakage and protection in two-party split learning.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training Label leakage and protection in two-party split learning

Reference 18

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Observation c35edeac-9102-4af6-8bd4-1070b2fe9e86 · outbound

This paper cites A survey on federated learning systems: Vision, hype and reality for data privacy and protection.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training A survey on federated learning systems: Vision, hype and reality for data privacy and protection

Reference 19

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Observation 8e968b4b-906a-49a7-9232-64b1605d0836 · outbound

This paper cites PyTorch RPC: Distributed Deep Learning Built on Tensor-Optimized Remote Procedure Calls.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training PyTorch RPC: Distributed Deep Learning Built on Tensor-Optimized Remote Procedure Calls

Reference 20

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Observation 1adb9842-7a1b-4389-842e-e8cc32ddee70 · outbound

This paper cites Prefix-tuning: Optimizing continuous prompts for generation.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training Prefix-tuning: Optimizing continuous prompts for generation

Reference 21

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Observation d7d2f0cd-6175-4699-9889-25a16381054d · outbound

This paper cites Privacy-preserving prompt tuning for large language model services.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training Privacy-preserving prompt tuning for large language model services

Reference 22

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Observation f918c9f1-b289-42ec-8c16-49e4fc5fa13c · outbound

This paper cites Differentially Private Low-Rank Adaptation of Large Language Model Using Federated Learning.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training Differentially Private Low-Rank Adaptation of Large Language Model Using Federated Learning

Reference 23

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Observation 8c33e2cf-7d43-4f12-bc24-6af45cd03da9 · outbound

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

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training Communication-Efficient Learning of Deep Networks from Decentralized Data

Reference 24

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Observation 65ccb2ac-4b4a-4e5b-b39a-1dfb9f98a779 · outbound

This paper cites N vidia confidential computing.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training N vidia confidential computing

Reference 25

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Observation e99c351d-9f3d-40ad-a3b5-e3456017b38e · outbound

This paper cites F ine-tuning S table D iffusion --- docs.octoai.cloud.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training F ine-tuning S table D iffusion --- docs.octoai.cloud

Reference 26

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Observation 917da093-c772-4862-bd23-6a648876008e · outbound

This paper cites O pen A I P latform --- platform.openai.com.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training O pen A I P latform --- platform.openai.com

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:43.679247Z

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Observation 4bc32ee8-2ff8-4196-a240-caa08d1d944e · outbound

This paper cites Unleashing the tiger: Inference attacks on split learning.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training Unleashing the tiger: Inference attacks on split learning

Reference 28

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Observation 4f8583ff-ef51-4b70-bf86-75af8c6eba9e · outbound

This paper cites Adapterfusion: Non-destructive task composition for transfer learning, 2021.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training Adapterfusion: Non-destructive task composition for transfer learning, 2021

Reference 29

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Observation ff748d11-4299-45a9-a1ae-79da8063d600 · outbound

This paper cites Bittensor: A peer-to-peer intelligence market, 2021.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training Bittensor: A peer-to-peer intelligence market, 2021

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:43.655761Z

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Observation 1e029f4c-89d7-4ebe-bb9d-d8c5d4a0b58e · outbound

This paper cites Just Fine-tune Twice: Selective Differential Privacy for Large Language Models.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training Just Fine-tune Twice: Selective Differential Privacy for Large Language Models

Reference 31

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Observation 6cf986de-01a8-46ab-ab67-99892a9762d3 · outbound

This paper cites Manning, Andrew Ng, and Christopher Potts.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training Manning, Andrew Ng, and Christopher Potts

Reference 32

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Observation 7e129fdf-cfcc-4b8b-a88b-36f6cb3aee0f · outbound

This paper cites Label Leakage and Protection from Forward Embedding in Vertical Federated Learning.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training Label Leakage and Protection from Forward Embedding in Vertical Federated Learning

Reference 33

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Observation ccf6f5ae-af72-4846-882a-ffb6569b20cd · outbound

This paper cites A survey on deep transfer learning.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training A survey on deep transfer learning

Reference 34

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Observation 64dee3b8-0963-4d1f-9adf-83ac2027e22a · outbound

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

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 35

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Observation 56e1f5d5-b9b5-44eb-b763-fed891478eb5 · outbound

This paper cites Split learning for health: Distributed deep learning without sharing raw patient data, 2018.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training Split learning for health: Distributed deep learning without sharing raw patient data, 2018

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:43.614268Z

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Observation e703753d-4e69-49c6-9236-6b70a3ea0c86 · outbound

This paper cites Reducing leakage in distributed deep learning for sensitive health data.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training Reducing leakage in distributed deep learning for sensitive health data

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:43.598840Z

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Observation e5e32f37-f9f0-4569-865b-234f9aa2d219 · outbound

This paper cites Pslf: Defending against label leakage in split learning.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training Pslf: Defending against label leakage in split learning

Reference 38

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Observation 8c9525aa-bbce-4319-a1af-2531b1c579a7 · outbound

This paper cites GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Reference 39

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Observation cb908729-ac0e-4e2e-bc06-0fb687dafcdf · outbound

This paper cites PrivateLoRA For Efficient Privacy Preserving LLM.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training PrivateLoRA For Efficient Privacy Preserving LLM

Reference 40

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Observation 2d0ac9c7-a549-41a7-a227-cc0a7a8fcdf4 · outbound

This paper cites Randomized response: A survey technique for eliminating evasive answer bias.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training Randomized response: A survey technique for eliminating evasive answer bias

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:43.571545Z

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Observation 6b86f629-c9b1-4394-aedf-db8eb80533a3 · outbound

This paper cites A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference

Reference 42

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Observation d81ac49d-6ff7-4a18-99dc-8d6e17a81b53 · outbound

This paper cites Offsite-Tuning: Transfer Learning without Full Model.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training Offsite-Tuning: Transfer Learning without Full Model

Reference 43

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Observation f6bae7e1-2438-4625-818f-b9b147b3a586 · outbound

This paper cites Federated machine learning: Concept and applications.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training Federated machine learning: Concept and applications

Reference 44

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Observation 0158a6a9-2b2c-4e38-973f-5b223204ddd3 · outbound

This paper cites Differentially private fine-tuning of language models.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training Differentially private fine-tuning of language models

Reference 45

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Observation a6f13ae2-74c8-473f-9cd2-c7c6bee40617 · outbound

This paper cites F ed PET uning: When federated learning meets the parameter-efficient tuning methods of pre-trained language models.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training F ed PET uning: When federated learning meets the parameter-efficient tuning methods of pre-trained language models

Reference 46

Resolution: verified exact; doi, observed 2026-08-11T10:26:43.000387Z

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Observation f93cb331-d791-4d9b-92d1-344d8c2fddbb · outbound

This paper cites Fedprompt: Communication-efficient and privacy preserving prompt tuning in federated learning, 2023.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training Fedprompt: Communication-efficient and privacy preserving prompt tuning in federated learning, 2023

Reference 47

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Observation da532418-9195-4628-8c3b-b32c076ba48f · outbound

This paper cites write newline.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training write newline

Reference 48

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Observation b4f04011-4cb0-4c38-bd92-8a3d597c5ab7 · outbound

This paper cites @esa (Ref.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training @esa (Ref

Reference 49

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Observation 1e2e9b99-6e63-42d3-983d-871194a36972 · outbound

This paper cites an unresolved cited work.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training Unresolved cited work

Reference 50

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Observation 4232eb65-237c-4e53-ab07-cc2ff7b752c5 · outbound

This paper cites an unresolved cited work.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training Unresolved cited work

Reference 51

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