Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices | Paper Citation Record

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This paper cites Attention is all you need.Advances in neural information processing systems, 30, 2017.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Attention is all you need.Advances in neural information processing systems, 30, 2017

Reference 1

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This paper cites BinaryBERT: Pushing the Limit of BERT Quantization.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices BinaryBERT: Pushing the Limit of BERT Quantization

Reference 2

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This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 3

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This paper cites Dynabert: Dynamic bert with adap- tive width and depth.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Dynabert: Dynamic bert with adap- tive width and depth

Reference 4

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This paper cites Language models are few-shot learn- ers.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Language models are few-shot learn- ers

Reference 5

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

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Communication- efficient learning of deep networks from decentralized data

Reference 6

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This paper cites FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks

Reference 7

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This paper cites Accelerating federated learning with data and model parallelism in edge com- puting.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Accelerating federated learning with data and model parallelism in edge com- puting

Reference 8

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Observation 3f9e8059-4bf6-4f9a-a2e0-6cb7586f7e6f · outbound

This paper cites Pretraining federated text models for next word prediction.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Pretraining federated text models for next word prediction

Reference 9

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Observation 4c023d99-a850-4997-8e0b-d48fd22970a3 · outbound

This paper cites FedAdapter: Efficient Federated Learning for Modern NLP.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices FedAdapter: Efficient Federated Learning for Modern NLP

Reference 10

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This paper cites Adaptive control of local up- dating and model compression for efficient federated learning.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Adaptive control of local up- dating and model compression for efficient federated learning

Reference 11

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This paper cites Pockengine: Sparse and efficient fine-tuning in a pocket.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Pockengine: Sparse and efficient fine-tuning in a pocket

Reference 12

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Observation 4a124c50-335e-4b91-9a6c-20f85311df09 · outbound

This paper cites A survey of on-device machine learning: An algorithms and learning theory perspective.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices A survey of on-device machine learning: An algorithms and learning theory perspective

Reference 13

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This paper cites Mergesfl: Split federated learning with feature merging and batch size regulation.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Mergesfl: Split federated learning with feature merging and batch size regulation

Reference 14

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Observation ede4a5e8-67ba-4048-aed3-66b1cc3474d9 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices LLaMA: Open and Efficient Foundation Language Models

Reference 15

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This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices LoRA: Low-Rank Adaptation of Large Language Models

Reference 16

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This paper cites Adaptive configuration for heterogeneous participants in decentralized federated learning.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Adaptive configuration for heterogeneous participants in decentralized federated learning

Reference 17

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This paper cites Oort: Efficient federated learn- ing via guided participant selection.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Oort: Efficient federated learn- ing via guided participant selection

Reference 18

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This paper cites Yoga: Adaptive layer-wise model aggregation for decentralized feder- ated learning.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Yoga: Adaptive layer-wise model aggregation for decentralized feder- ated learning

Reference 19

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

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Fedpetuning: When federated learning meets the parameter-efficient tuning methods of pre-trained language models

Reference 20

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This paper cites Parameter- efficient transfer learning for nlp.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Parameter- efficient transfer learning for nlp

Reference 21

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This paper cites Parameter-Efficient Fine-Tuning without Introducing New Latency.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Parameter-Efficient Fine-Tuning without Introducing New Latency

Reference 22

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This paper cites CaraServe: CPU-Assisted and Rank-Aware LoRA Serving for Generative LLM Inference.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices CaraServe: CPU-Assisted and Rank-Aware LoRA Serving for Generative LLM Inference

Reference 23

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This paper cites A survey on sentiment anal- ysis methods, applications, and challenges.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices A survey on sentiment anal- ysis methods, applications, and challenges

Reference 24

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This paper cites Deep learning–based text classification: a comprehen- sive review.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Deep learning–based text classification: a comprehen- sive review

Reference 25

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This paper cites Adaptive budget allocation for parameter-efficient fine- tuning.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Adaptive budget allocation for parameter-efficient fine- tuning

Reference 26

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This paper cites Heterogeneous lora for fed- erated fine-tuning of on-device foundation models.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Heterogeneous lora for fed- erated fine-tuning of on-device foundation models

Reference 27

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This paper cites Autofl: Enabling heterogeneity-aware energy efficient federated learning.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Autofl: Enabling heterogeneity-aware energy efficient federated learning

Reference 28

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Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Tackling system and statistical heterogeneity for federated learning with adaptive client sampling

Reference 29

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This paper cites Wikimedia downloads.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Wikimedia downloads

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Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Unresolved cited work

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Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices FwdLLM: Efficient FedLLM using Forward Gradient

Reference 32

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Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Decoupled Weight Decay Regularization

Reference 33

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This paper cites Challenges and Applications of Large Language Models.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Challenges and Applications of Large Language Models

Reference 34

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Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Flexora: Flexible low rank adap- tation for large language models

Reference 35

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Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Spottune: transfer learning through adaptive fine-tuning

Reference 36

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Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning

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Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices RoBERTa: A Robustly Optimized BERT Pretraining Approach

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Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Reference 39

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This paper cites Increasing Model Capacity for Free: A Simple Strategy for Parameter Efficient Fine-tuning.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Increasing Model Capacity for Free: A Simple Strategy for Parameter Efficient Fine-tuning

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Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Lora vs full fine-tuning: An illu- sion of equivalence

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Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Layer-wised model aggregation for personalized feder- ated learning

Reference 42

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Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Higher Layers Need More LoRA Experts

Reference 43

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Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Federated Learning: Strategies for Improving Communication Efficiency

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Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Federated learning for keyword spotting

Reference 45

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This paper cites Predictable 802.11 packet delivery from wire- less channel measurements.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Predictable 802.11 packet delivery from wire- less channel measurements

Reference 46

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Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Linkforecast: Cellu- lar link bandwidth prediction in lte networks

Reference 47

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This paper cites When Federated Learning Meets Pre-trained Language Models' Parameter-Efficient Tuning Methods.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices When Federated Learning Meets Pre-trained Language Models' Parameter-Efficient Tuning Methods

Reference 48

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Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices A survey on optimized implementation of deep learning models on the nvidia jetson platform

Reference 49

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Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Docker: lightweight linux containers for consistent development and deployment

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Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Building a virtual system of systems using docker swarm in multiple clouds

Reference 51

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This paper cites Py- torch: An imperative style, high-performance deep learn- ing library.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Py- torch: An imperative style, high-performance deep learn- ing library

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Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Open mpi: Goals, concept, and de- sign of a next generation mpi implementation

Reference 53

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This paper cites http://dast.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices http://dast

Reference 54

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This paper cites Transformers: State-of-the-art natural language process- ing.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Transformers: State-of-the-art natural language process- ing

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:46:34.177505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6ab859da-128c-434d-959c-99a366939637 · outbound

This paper cites DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing

Reference 56

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Observation 90b42e43-bb5d-4aaa-b2bf-50126fc59039 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Measuring Massive Multitask Language Understanding

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-10T23:46:33.210266Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd7d40eb-5c01-4729-8680-caedae851430 · outbound

This paper cites Deep neu- ral solver for math word problems.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Deep neu- ral solver for math word problems

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:46:34.148854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation fa5054e9-0d67-4de7-a589-160b70bf329e · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Training Verifiers to Solve Math Word Problems

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-10T23:46:33.222177Z

Source-reported events for the cited work

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Observation 99377caa-c613-4c93-a35c-d54b82e0c64a · outbound

This paper cites Language mod- els are unsupervised multitask learners.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Language mod- els are unsupervised multitask learners

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-10T23:46:33.228465Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1fbe8233-5c33-4d17-aa46-1194cd14bae4 · outbound

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

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-10T23:46:33.234782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed6953ba-621a-46ac-a2a2-10f2463e58d0 · outbound

This paper cites Fedprompt: Communication-efficient and privacy-preserving prompt tuning in federated learn- ing.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Fedprompt: Communication-efficient and privacy-preserving prompt tuning in federated learn- ing

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:46:34.107968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4ba63c67-fb90-4a76-8c63-2630458e258b · outbound

This paper cites Qlora: Efficient finetuning of quan- tized llms.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices Qlora: Efficient finetuning of quan- tized llms

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:46:34.088771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e82d55e9-5a7b-412f-a490-5c6fd7ae898e · outbound

This paper cites LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models.

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-10T23:46:33.253430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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