Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach | Paper Citation Record

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This paper cites Language models are unsupervised multitask learners.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Language models are unsupervised multitask learners

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This paper cites Bert: Pre- training of deep bidirectional transformers for language understand- ing.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Bert: Pre- training of deep bidirectional transformers for language understand- ing

Reference 2

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Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach The llama 3 herd of models

Reference 3

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This paper cites Qwen2.5-Omni Technical Report.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Qwen2.5-Omni Technical Report

Reference 4

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This paper cites Feddat: An approach for foundation model finetuning in multi-modal heteroge- neous federated learning.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Feddat: An approach for foundation model finetuning in multi-modal heteroge- neous federated learning

Reference 5

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This paper cites Efficient federated learning for modern nlp.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Efficient federated learning for modern nlp

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This paper cites Federatedscope-llm: A comprehensive package for fine-tuning large language models in federated learning.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Federatedscope-llm: A comprehensive package for fine-tuning large language models in federated learning

Reference 7

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This paper cites The impact of gdpr on global technology development.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach The impact of gdpr on global technology development

Reference 8

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This paper cites Openfedllm: Training large language models on decentral- ized private data via federated learning.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Openfedllm: Training large language models on decentral- ized private data via federated learning

Reference 9

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This paper cites The Future of Large Language Model Pre-training is Federated.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach The Future of Large Language Model Pre-training is Federated

Reference 10

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This paper cites Safely learning with private data: A federated learning framework for large language model.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Safely learning with private data: A federated learning framework for large language model

Reference 11

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This paper cites Dual-personalizing adapter for federated foundation models.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Dual-personalizing adapter for federated foundation models

Reference 12

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This paper cites Adapter- fusion: Non-destructive task composition for transfer learning.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Adapter- fusion: Non-destructive task composition for transfer learning

Reference 13

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This paper cites DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptation.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptation

Reference 14

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This paper cites Memetic federated learning for biomedical natural language processing.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Memetic federated learning for biomedical natural language processing

Reference 15

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This paper cites An in-depth evaluation of federated learning on biomedical natural lan- guage processing for information extraction.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach An in-depth evaluation of federated learning on biomedical natural lan- guage processing for information extraction

Reference 16

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This paper cites Fedlegal: The first real-world federated learning benchmark for legal nlp.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Fedlegal: The first real-world federated learning benchmark for legal nlp

Reference 17

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This paper cites Prompt federated learning for weather forecasting: Toward foundation models on meteorological data.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Prompt federated learning for weather forecasting: Toward foundation models on meteorological data

Reference 18

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This paper cites Flexible and secure code deployment in federated learning using large language models: Prompt engineer- ing to enhance malicious code detection.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Flexible and secure code deployment in federated learning using large language models: Prompt engineer- ing to enhance malicious code detection

Reference 19

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This paper cites F- codellm: A federated learning framework for adapting large language models to practical software development.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach F- codellm: A federated learning framework for adapting large language models to practical software development

Reference 20

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This paper cites Code summarization without direct access to code-towards exploring federated llms for software engi- neering.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Code summarization without direct access to code-towards exploring federated llms for software engi- neering

Reference 21

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This paper cites Recursive deep models for semantic compositionality over a sentiment treebank.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Recursive deep models for semantic compositionality over a sentiment treebank

Reference 22

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This paper cites Learning multiple layers of features from tiny images.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Learning multiple layers of features from tiny images

Reference 23

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This paper cites Accuracy-privacy trade-off in deep ensemble: A membership inference perspective.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Accuracy-privacy trade-off in deep ensemble: A membership inference perspective

Reference 24

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This paper cites Learning-based difficulty cal- ibration for enhanced membership inference attacks.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Learning-based difficulty cal- ibration for enhanced membership inference attacks

Reference 25

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This paper cites Membership inference attacks by exploiting loss trajectory.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Membership inference attacks by exploiting loss trajectory

Reference 26

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This paper cites Com- parative analysis of membership inference attacks in federated and centralized learning.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Com- parative analysis of membership inference attacks in federated and centralized learning

Reference 27

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This paper cites Efficient privacy auditing in federated learning.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Efficient privacy auditing in federated learning

Reference 28

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This paper cites Towards label-only membership inference attack against pre-trained large language models.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Towards label-only membership inference attack against pre-trained large language models

Reference 29

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This paper cites Towards sparsified federated neuroimaging models via weight pruning.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Towards sparsified federated neuroimaging models via weight pruning

Reference 30

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This paper cites Defending Membership Inference Attacks via Privacy-aware Sparsity Tuning.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Defending Membership Inference Attacks via Privacy-aware Sparsity Tuning

Reference 31

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This paper cites Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning

Reference 32

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This paper cites Effective passive membership inference attacks in federated learning against overparameterized models.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Effective passive membership inference attacks in federated learning against overparameterized models

Reference 33

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This paper cites Neural network accept- ability judgments.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Neural network accept- ability judgments

Reference 34

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This paper cites Yelp Dataset Challenge: Review Rating Prediction.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Yelp Dataset Challenge: Review Rating Prediction

Reference 35

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This paper cites Recursive deep models for semantic compositionality over a sentiment treebank.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Recursive deep models for semantic compositionality over a sentiment treebank

Reference 36

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This paper cites Learning word vectors for sentiment analysis.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Learning word vectors for sentiment analysis

Reference 37

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This paper cites Communication-efficient learning of deep networks from decentral- ized data.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Communication-efficient learning of deep networks from decentral- ized data

Reference 38

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This paper cites Save it all: Enabling full parameter tuning for federated large language models via cycle block gradient descent.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Save it all: Enabling full parameter tuning for federated large language models via cycle block gradient descent

Reference 39

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This paper cites Online model compression for federated learning with large models.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Online model compression for federated learning with large models

Reference 40

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This paper cites Parameter-efficient transfer learning for nlp.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Parameter-efficient transfer learning for nlp

Reference 41

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This paper cites Federated learning of large language models with parameter-efficient prompt tuning and adaptive optimization.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Federated learning of large language models with parameter-efficient prompt tuning and adaptive optimization

Reference 42

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This paper cites P- tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach P- tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks

Reference 43

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This paper cites How to Combine Membership-Inference Attacks on Multiple Updated Models.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach How to Combine Membership-Inference Attacks on Multiple Updated Models

Reference 44

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This paper cites Fedmia: An effective membership inference attack exploiting.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Fedmia: An effective membership inference attack exploiting

Reference 45

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This paper cites Exploiting unintended feature leakage in collaborative learning.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Exploiting unintended feature leakage in collaborative learning

Reference 46

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Observation 1f60df64-9a5c-4f46-a911-85cb2b4a4916 · outbound

This paper cites Perfectly ac- curate membership inference by a dishonest central server in federated learning.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Perfectly ac- curate membership inference by a dishonest central server in federated learning

Reference 47

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Observation 807f709d-1233-48b3-9a2e-3dd66e85e617 · outbound

This paper cites Analysis of privacy leakage in federated large language models.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Analysis of privacy leakage in federated large language models

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T14:55:42.191543Z

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Observation 87a8da85-9b7e-4df3-ab89-71e841cb47ef · outbound

This paper cites Dager: Exact gradient inversion for large language models.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Dager: Exact gradient inversion for large language models

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T14:55:42.202460Z

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This paper cites Qwen2 Technical Report.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Qwen2 Technical Report

Reference 50

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Observation d1e42e54-1108-482b-80c6-a28b7fa788db · outbound

This paper cites Towards practical few-shot federated nlp.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Towards practical few-shot federated nlp

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T14:55:42.206451Z

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Observation c12c2dc4-a164-449c-afb3-044b82d4f6e2 · outbound

This paper cites {FwdLLM}: Efficient federated finetuning of large language models with perturbed infer- ences.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach {FwdLLM}: Efficient federated finetuning of large language models with perturbed infer- ences

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T14:55:42.214309Z

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Observation afff12b5-9b4c-4bcb-a14f-7a05404f3e15 · outbound

This paper cites A Framework for Evaluating Gradient Leakage Attacks in Federated Learning.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach A Framework for Evaluating Gradient Leakage Attacks in Federated Learning

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-09T22:49:16.392465Z

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Observation 429ff21e-0808-4396-9094-ea143b28f8b2 · outbound

This paper cites Deep leakage from gradients.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Deep leakage from gradients

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T14:55:42.246693Z

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Observation aff9b642-a639-4a22-9542-621f3d405648 · outbound

This paper cites Fedbert: When federated learning meets pre-training.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Fedbert: When federated learning meets pre-training

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T14:55:42.123080Z

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Observation 44920023-b8a8-4920-86e2-41f4bd516be4 · outbound

This paper cites Qa-lora: Quantization-aware low-rank adap- tation of large language models.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Qa-lora: Quantization-aware low-rank adap- tation of large language models

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T14:55:42.126490Z

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Observation fae9d938-61b7-4cb8-81df-9be3e5e366e4 · outbound

This paper cites Low-parameter federated learning with large language models.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Low-parameter federated learning with large language models

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T14:55:42.115893Z

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Observation db8a7692-20d4-46cc-aaf3-27e2b85d7adc · outbound

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

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach FDLoRA: Personalized Federated Learning of Large Language Model via Dual LoRA Tuning

Reference 58

Resolution: metadata mismatch; arxiv_id, observed 2026-07-07T03:18:45.476301Z

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Observation cf9327ec-777f-4389-8928-6b4d981bf8a4 · outbound

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

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Federated fine- tuning of large language models under heterogeneous tasks and client resources

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T14:55:42.104362Z

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Observation 073100b6-8528-4bf2-bbd0-724cf7266492 · outbound

This paper cites Fedrdma: Communication-efficient cross-silo federated LLM via chunked RDMA transmission.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Fedrdma: Communication-efficient cross-silo federated LLM via chunked RDMA transmission

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T14:55:42.109274Z

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Observation a833540d-0e70-4e4d-93a0-1e5bd7782540 · outbound

This paper cites Membership inference attacks and defenses in federated learning: A survey.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Membership inference attacks and defenses in federated learning: A survey

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T14:55:42.112702Z

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Observation a96e2ab7-1d42-473b-821e-090fcd60bd4e · outbound

This paper cites Agrevader: Poisoning membership inference against byzantine-robust federated learning.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Agrevader: Poisoning membership inference against byzantine-robust federated learning

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T14:55:42.096529Z

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Observation c3d4f291-ae53-4216-8b9c-acf217b23768 · outbound

This paper cites Systematic evaluation of privacy risks of machine learning models.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Systematic evaluation of privacy risks of machine learning models

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T14:55:42.089984Z

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Observation 4b5e7296-4ca3-436b-8d96-c49aaaad2fa2 · outbound

This paper cites Federated few-shot learning for mobile nlp.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Federated few-shot learning for mobile nlp

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T14:55:42.093284Z

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Observation 0efefe45-6731-4bed-b2b6-973713f8159b · outbound

This paper cites Bitfit: Simple parameter- efficient fine-tuning for transformer-based masked language-models.

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Bitfit: Simple parameter- efficient fine-tuning for transformer-based masked language-models

Reference 65

Resolution: malformed identifier; raw_fallback, observed 2026-05-23T14:55:42.100277Z

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