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Federated Learning has recently been utilized to collaboratively fine-tune foundation models across multiple clients.
Algorithm as 136: A k-means clustering algorithm
John A Hartigan and Manchek A Wong · 1979
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Application of computerized adaptive testing to educational problems
David J Weiss and G Gage Kingsbury · 1984
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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin · 2004
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Differential privacy
Cynthia Dwork · 2006
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Describing textures in the wild
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2014
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
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Practical secure aggregation for federated learning on user-held data
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2016
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Federated learning: Strategies for improving communication efficiency
Jakub Konecnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
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Asynchrony begets momentum, with an application to deep learning
Ioannis Mitliagkas, Ce Zhang, Stefan Hadjis, and Christopher Ré · 2016
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
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To prune, or not to prune: exploring the efficacy of pruning for model compression
Michael Zhu and Suyog Gupta · 2017
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Glue: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Fedmd: Heterogenous federated learning via model distillation
Daliang Li and Junpu Wang · 2019
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Moment matching for multi-source domain adaptation
Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang · 2019
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Heterofl: Computation and communication efficient federated learning for heterogeneous clients
Enmao Diao, Jie Ding, and Vahid Tarokh · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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Fltrust: Byzantine-robust federated learning via trust bootstrapping
Xiaoyu Cao, Minghong Fang, Jia Liu, and Neil Gong · 2021
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Towards a unified view of parameter-efficient transfer learning
Junxian He, Chunting Zhou, Xuezhe Ma, Taylor Berg-Kirkpatrick, and Graham Neubig · 2021
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Fjord: Fair and accurate federated learning under heterogeneous targets with ordered dropout
Samuel Horvath, Stefanos Laskaridis, Mario Almeida, Ilias Leontiadis, Stylianos Venieris, and Nicholas Lane · 2021
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Lora: Low-rank adaptation of large language models
Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al · 2021
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
Cited alongside, same era.
Sageflow: Robust federated learning against both stragglers and adversaries
Jungwuk Park, Dong-Jun Han, Minseok Choi, and Jaekyun Moon · 2021
Cited alongside, same era.
Dynamicvit: Efficient vision transformers with dynamic token sparsification
Yongming Rao, Wenliang Zhao, Benlin Liu, Jiwen Lu, Jie Zhou, and Cho-Jui Hsieh · 2021
Cited alongside, same era.
Fast-convergent federated learning with adaptive weighting
Hongda Wu and Ping Wang · 2021
Cited alongside, same era.
Fed2: Feature-aligned federated learning
Fuxun Yu, Weishan Zhang, Zhuwei Qin, Zirui Xu, Di Wang, Chenchen Liu, Zhi Tian, and Xiang Chen · 2021
Cited alongside, same era.
Lexglue: A benchmark dataset for legal language understanding in english
Ilias Chalkidis, Abhik Jana, Dirk Hartung, Michael Bommarito, Ion Androutsopoulos, Daniel Martin Katz, and Nikolaos Aletras · 2022
Federated foundation models: Privacy-preserving and collaborative learning for large models
Sixing Yu, J Pablo Muñoz, and Ali Jannesari · 2023
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Towards building the federatedgpt: Federated instruction tuning
Jianyi Zhang, Saeed Vahidian, Martin Kuo, Chunyuan Li, Ruiyi Zhang, Tong Yu, Guoyin Wang, and Yiran Chen · 2023
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Memory-adaptive depth-wise heterogenous federated learning
Kai Zhang, Yutong Dai, Hongyi Wang, Eric Xing, Xun Chen, and Lichao Sun · 2023
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Fedpetuning: When federated learning meets the parameter-efficient tuning methods of pre-trained language models
Zhuo Zhang, Yuanhang Yang, Yong Dai, Qifan Wang, Yue Yu, Lizhen Qu, and Zenglin Xu · 2023
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Fedprompt: Communication-efficient and privacy-preserving prompt tuning in federated learning
Haodong Zhao, Wei Du, Fangqi Li, Peixuan Li, and Gongshen Liu · 2023
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Cited alongside, same era.
Which layer is learning faster? a systematic exploration of layer-wise convergence rate for deep neural networks
Yixiong Chen, Alan Yuille, and Zongwei Zhou · 2022
Cited alongside, same era.
Delta tuning: A comprehensive study of parameter efficient methods for pre-trained language models
Ning Ding, Yujia Qin, Guang Yang, Fuchao Wei, Zonghan Yang, Yusheng Su, Shengding Hu, Yulin Chen, Chi-Min Chan, Weize Chen, et al · 2022
Cited alongside, same era.
Depthfl: Depthwise federated learning for heterogeneous clients
Minjae Kim, Sangyoon Yu, Suhyun Kim, and Soo-Mook Moon · 2022
Cited alongside, same era.
No one left behind: Inclusive federated learning over heterogeneous devices
Ruixuan Liu, Fangzhao Wu, Chuhan Wu, Yanlin Wang, Lingjuan Lyu, Hong Chen, and Xing Xie · 2022
Cited alongside, same era.
A study of the attention abnormality in trojaned berts
Weimin Lyu, Songzhu Zheng, Tengfei Ma, and Chao Chen · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
Cited alongside, same era.
A comprehensive survey on pretrained foundation models: A history from bert to chatgpt
Ce Zhou, Qian Li, Chen Li, Jun Yu, Yixin Liu, Guangjing Wang, Kai Zhang, Cheng Ji, Qiben Yan, Lifang He, et al · 2023
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Autopeft: Automatic configuration search for parameter-efficient fine-tuning
Han Zhou, Xingchen Wan, Ivan Vulić, and Anna Korhonen · 2023
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When foundation model meets federated learning: Motivations, challenges, and future directions
Weiming Zhuang, Chen Chen, and Lingjuan Lyu · 2023
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Slimfit: Memory-efficient fine-tuning of transformer-based models using training dynamics
Arash Ardakani, Altan Haan, Shangyin Tan, Doru Thom Popovici, Alvin Cheung, Costin Iancu, and Koushik Sen · 2024
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Federated fine-tuning of large language models under heterogeneous tasks and client resources
Jiamu Bai, Daoyuan Chen, Bingchen Qian, Liuyi Yao, and Yaliang Li · 2024
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Strong baselines for parameter-efficient few-shot fine-tuning
Samyadeep Basu, Shell Hu, Daniela Massiceti, and Soheil Feizi · 2024
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Data-juicer: A one-stop data processing system for large language models
Daoyuan Chen, Yilun Huang, Zhijian Ma, Hesen Chen, Xuchen Pan, Ce Ge, Dawei Gao, Yuexiang Xie, Zhaoyang Liu, Jinyang Gao, et al · 2024
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Heterogeneous lora for federated fine-tuning of on-device foundation models
Yae Jee Cho, Luyang Liu, Zheng Xu, Aldi Fahrezi, and Gauri Joshi · 2024
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Higher layers need more lora experts
Chongyang Gao, Kezhen Chen, Jinmeng Rao, Baochen Sun, Ruibo Liu, Daiyi Peng, Yawen Zhang, Xiaoyuan Guo, Jie Yang, and VS Subrahmanian · 2024
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Lisa: Layerwise importance sampling for memory-efficient large language model fine-tuning
Rui Pan, Xiang Liu, Shizhe Diao, Renjie Pi, Jipeng Zhang, Chi Han, and Tong Zhang · 2024
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Improving loRA in privacy-preserving federated learning
Youbang Sun, Zitao Li, Yaliang Li, and Bolin Ding · 2024
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Flora: Federated fine-tuning large language models with heterogeneous low-rank adaptations
Ziyao Wang, Zheyu Shen, Yexiao He, Guoheng Sun, Hongyi Wang, Lingjuan Lyu, and Ang Li · 2024
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Fedbiot: Llm local fine-tuning in federated learning without full model
Feijie Wu, Zitao Li, Yaliang Li, Bolin Ding, and Jing Gao · 2024
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Fedfmsl: Federated learning of foundations models with sparsely activated lora
Panlong Wu, Kangshuo Li, Ting Wang, Yanjie Dong, Victor CM Leung, and Fangxin Wang · 2024
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Fed-pilot: Optimizing lora assignment for efficient federated foundation model fine-tuning
Zikai Zhang, Jiahao Xu, Ping Liu, and Rui Hu · 2024
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Flowertune: A cross-domain benchmark for federated fine-tuning of large language models
Yan Gao, Massimo Roberto Scamarcia, Javier Fernandez-Marques, Mohammad Naseri, Chong Shen Ng, Dimitris Stripelis, Zexi Li, Tao Shen, Jiamu Bai, Daoyuan Chen, Zikai Zhang, et al · 2025
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Fedra: A random allocation strategy for federated tuning to unleash the power of heterogeneous clients
Shangchao Su, Bin Li, and Xiangyang Xue · 2025
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Fedcust: Offloading hyperparameter customization for federated learning
Syed Zawad, Xiaolong Ma, Jun Yi, Cheng Li, Minjia Zhang, Lei Yang, Feng Yan, and Yuxiong He · 2025
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