{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T09:44:43Z","timestamp":1782726283161,"version":"3.54.5"},"reference-count":43,"publisher":"IEEE","license":[{"start":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T00:00:00Z","timestamp":1771200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T00:00:00Z","timestamp":1771200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,2,16]]},"DOI":"10.1109\/icnc68183.2026.11416880","type":"proceedings-article","created":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T19:55:54Z","timestamp":1773086154000},"page":"505-511","source":"Crossref","is-referenced-by-count":1,"title":["PrivLoRA: Enhancing Privacy in LoRA-Based Fine-Tuning of Large Language Models for Federated Learning"],"prefix":"10.1109","author":[{"given":"Bayan","family":"Alzahrani","sequence":"first","affiliation":[{"name":"Colorado School of Mines,Computer Science,Golden,Colorado"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dejun","family":"Yang","sequence":"additional","affiliation":[{"name":"Colorado School of Mines,Computer Science,Golden,Colorado"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1877","article-title":"Language models are few-shot learners","volume":"33","author":"Brown","year":"2020","journal-title":"Advances in neural information processing systems"},{"key":"ref2","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"McMahan","year":"2017","journal-title":"Artificial intelligence and statistics"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.18653\/vl\/N19-142"},{"key":"ref4","article-title":"Pathways language model (palm): Scaling to 540 billion parameters for breakthrough performance","author":"Narang","year":"2022"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.52202\/075280-0911"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.4018\/IJSPPC.325475"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-023-00626-4"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-acl.632"},{"key":"ref9","article-title":"Slora: Federated parameter efficient fine-tuning of language models","volume-title":"International Workshop on FL in the Age of Foundation Models in Conjunction with NeurIPS","author":"Babakniya"},{"issue":"2","key":"ref10","first-page":"3","article-title":"Lora: Low-rank adaptation of large language models","volume":"1","author":"Hu","year":"2022","journal-title":"ICLR"},{"key":"ref11","article-title":"Federa: Efficient fine-tuning of language models in federated learning leveraging weight decomposition","author":"Yan","year":"2024","journal-title":"arXiv preprint arXiv:2404.18848"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/s40747-023-00978-9"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/BF02288367"},{"key":"ref15","article-title":"Exploring parameter-efficient fine-tuning for improving communication efficiency in federated learning","author":"Sun","year":"2022","journal-title":"arXiv preprint arXiv:2210.01708"},{"key":"ref16","article-title":"When federated learning meets pre-trained language models\u2019 parameter-efficient tuning methods","author":"Zhang","year":"2022","journal-title":"arXiv preprint arXiv:2212.10025"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-024-40663-9"},{"issue":"2","key":"ref18","article-title":"A survey of large language models","volume":"1","author":"Zhao","year":"2023","journal-title":"arXiv preprint arXiv:2303.18223"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref20","article-title":"Improving language understanding by generative pre-training","author":"Radford","year":"2018","journal-title":"USA"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICMC60390.2024.00008"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/3539597.3575792"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.hcc.2024.100211"},{"key":"ref24","article-title":"Group equivariant stand-alone self-attention for vision","volume-title":"International Conference on Learning Representations","author":"Romero"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i14.17533"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2022.06.062"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3142899"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00982"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01007"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.126831"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/WACV57701.2024.00061"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.568"},{"key":"ref33","article-title":"Privacy-preserving low-rank adaptation for latent diffusion models","author":"Luo","year":"2024"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.52202\/079017-3846"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-13190-5_1"},{"key":"ref36","article-title":"Roberta: A robustly optimized bert pretraining approach","author":"Liu","year":"2019","journal-title":"arXiv preprint arXiv:1907.11692"},{"key":"ref37","article-title":"Llama: Open and efficient foundation language models","author":"Touvron","year":"2023","journal-title":"arXiv preprint arXiv:2302.13971"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W18-5446"},{"key":"ref39","article-title":"Huggingface\u2019s transformers: State-of-the-art natural language processing","author":"Wolf","year":"2019","journal-title":"arXiv preprint arXiv:1910.03771"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671573"},{"key":"ref41","article-title":"Opacus: User-friendly differential privacy library in pytorch","volume-title":"NeurIPS 2021 Workshop Privacy in Machine Learning","author":"Yousefpour"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/3682068"},{"key":"ref43","article-title":"Improving lora in privacy-preserving federated learning","volume-title":"The Twelfth International Conference on Learning Representations","author":"Sun"}],"event":{"name":"2026 International Conference on Computing, Networking and Communications (ICNC)","location":"Maui, HI, USA","start":{"date-parts":[[2026,2,16]]},"end":{"date-parts":[[2026,2,19]]}},"container-title":["2026 International Conference on Computing, Networking and Communications (ICNC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11416824\/11416825\/11416880.pdf?arnumber=11416880","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T05:27:31Z","timestamp":1773120451000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11416880\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,16]]},"references-count":43,"URL":"https:\/\/doi.org\/10.1109\/icnc68183.2026.11416880","relation":{},"subject":[],"published":{"date-parts":[[2026,2,16]]}}}