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Over the past few years, the landscape of Artificial Intelligence (AI) has been reshaped by the emergence of Foundation Models (FMs).
Federated collaborative filtering for privacy-preserving personalized recommendation system
Muhammad Ammad-Ud-Din, Elena Ivannikova, Suleiman A. Khan, Were Oyomno, Qiang Fu, Kuan Eeik Tan, and Adrian Flanagan. 2019 · 1901
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Language models are few-shot learners
Tom Brown et al. 2020 · 1901
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How multilingual is multilingual bert?
Telmo Pires, Eva Schlinger, and Dan Garrette. 2019 · 1906
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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Detailed comparison of communication efficiency of split learning and federated learning
Abhishek Singh, Praneeth Vepakomma, Otkrist Gupta, and Ramesh Raskar. 2019 · 1909
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Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2020 · 1910
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Multivariate stochastic approximation using a simultaneous perturbation gradient approximation
J.C. Spall. 1992 · 1992
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Completely derandomized self-adaptation in evolution strategies
Nikolaus Hansen and Andreas Ostermeier. 2001 · 2001
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Visual transformers: Token-based image representation and processing for computer vision
Bichen Wu, Chenfeng Xu, Xiaoliang Dai, Alvin Wan, Peizhao Zhang, Zhicheng Yan, Masayoshi Tomizuka, Joseph Gonzalez, Kurt Keutzer, and Peter Vajda. 2020a · 2006
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Fedml: A research library and benchmark for federated machine learning
Chaoyang He, Songze Li, Jinhyun So, Xiao Zeng, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu, Xinghua Zhu, Jianzong Wang, Li Shen, Peilin Zhao, Yan Kang, Yang Liu, Ramesh Raskar, Qiang Yang, Murali Annavaram, and Salman Avestimehr. 2020 · 2007
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Multilingual translation with extensible multilingual pretraining and finetuning
Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, and Angela Fan. 2020 · 2008
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Recommender systems survey
J. Bobadilla, F. Ortega, A. Hernando, and A. Gutiérrez. 2013 · 2013
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Optimal rates for zero-order convex optimization: The power of two function evaluations
John C Duchi, Michael I Jordan, Martin J Wainwright, and Andre Wibisono. 2015 · 2015
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Regulation (eu) 2016/679 of the european parliament and of the council
GDPR. 2016 · 2016
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Machine learning with adversaries: Byzantine tolerant gradient descent
Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui, and Julien Stainer. 2017 · 2017
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Distributed statistical machine learning in adversarial settings: Byzantine gradient descent
Yudong Chen, Lili Su, and Jiaming Xu. 2017 · 2017
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Hypernetworks
David Ha, Andrew M. Dai, and Quoc V. Le. 2017 · 2017
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Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation
Melvin Johnson, Mike Schuster, Quoc V. Le, Maxim Krikun, Yonghui Wu, Zhifeng Chen, Nikhil Thorat, Fernanda Viégas, Martin Wattenberg, Greg Corrado, Macduff Hughes, and Jeffrey Dean. 2017 · 2017
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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 · 2017
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Turning your weakness into a strength: Watermarking deep neural networks by backdooring
Yossi Adi, Carsten Baum, Moustapha Cisse, Benny Pinkas, and Joseph Keshet. 2018 · 2018
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Byzantine-robust distributed learning: Towards optimal statistical rates
Dong Yin, Yudong Chen, Ramchandran Kannan, and Peter Bartlett. 2018 · 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 · 2019
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin. 2019 · 2019
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Parameter-efficient transfer learning for NLP
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019 · 2019
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Smpai: Secure multi-party computation for federated learning
Vaikkunth Mugunthan, Antigoni Polychroniadou, David Byrd, and Tucker Hybinette Balch. 2019 · 2019
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Rewon child, david luan, dario amodei, and ilya sutskever. 2019
Alec Radford and Jeffrey Wu. 2019 · 2019
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Deep leakage from gradients
Ligeng Zhu, Zhijian Liu, and Song Han. 2019 · 2019
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When foundation model meets federated learning: Motivations, challenges, and future directions
Weiming Zhuang, Chen Chen, and Lingjuan Lyu. 2023 · 2019
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wav2vec 2.0: A framework for self-supervised learning of speech representations
Alexei Baevski, Yuhao Zhou, Abdelrahman Mohamed, and Michael Auli. 2020 · 2020
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How to backdoor federated learning
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov. 2020 · 2020
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Unsupervised cross-lingual representation learning at scale
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020 · 2020
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Local model poisoning attacks to byzantine-robust federated learning
Minghong Fang, Xiaoyu Cao, Jinyuan Jia, and Neil Gong. 2020 · 2020
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Inverting gradients - how easy is it to break privacy in federated learning?
Jonas Geiping, Hartmut Bauermeister, Hannah Dröge, and Michael Moeller. 2020 · 2020
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Gradientless descent: High-dimensional zeroth-order optimization
Daniel Golovin, John Karro, Greg Kochanski, Chansoo Lee, Xingyou Song, and Qiuyi Zhang. 2020 · 2020
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Don’t stop pretraining: Adapt language models to domains and tasks
Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, and Noah A. Smith. 2020 · 2020
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A taxonomy of attacks on federated learning
Malhar S Jere, Tyler Farnan, and Farinaz Koushanfar. 2020 · 2020
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Lessons from archives: Strategies for collecting sociocultural data in machine learning
Eun Seo Jo and Timnit Gebru. 2020 · 2020
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Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. 2020 · 2020
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Early detection of type-2 diabetes using federated learning
M Lincy and A Meena Kowshalya. 2020 · 2020
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A primer on zeroth-order optimization in signal processing and machine learning: Principals, recent advances, and applications
Sijia Liu, Pin-Yu Chen, Bhavya Kailkhura, Gaoyuan Zhang, Alfred O. Hero III, and Pramod K. Varshney. 2020 · 2020
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The future of digital health with federated learning
Nicola Rieke, Jonny Hancox, Wenqi Li, Fausto Milletari, Holger R Roth, Shadi Albarqouni, Spyridon Bakas, Mathieu N Galtier, Bennett A Landman, Klaus Maier-Hein, et al. 2020 · 2020
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Federated learning with differential privacy: Algorithms and performance analysis
Kang Wei, Jun Li, Ming Ding, Chuan Ma, Howard H. Yang, Farhad Farokhi, Shi Jin, Tony Q. S. Quek, and H. Vincent Poor. 2020 · 2020
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Are all languages created equal in multilingual BERT?
Shijie Wu and Mark Dredze. 2020 · 2020
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Dba: Distributed backdoor attacks against federated learning
Chulin Xie, Keli Huang, Pin-Yu Chen, and Bo Li. 2020 · 2020
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BatchCrypt: Efficient homomorphic encryption for Cross-Silo federated learning
Chengliang Zhang, Suyi Li, Junzhe Xia, Wei Wang, Feng Yan, and Yang Liu. 2020 · 2020
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Federated learning based on dynamic regularization
Durmus Alp Emre Acar, Yue Zhao, Ramon Matas, Matthew Mattina, Paul Whatmough, and Venkatesh Saligrama. 2021 · 2021
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Intrinsic dimensionality explains the effectiveness of language model fine-tuning
Armen Aghajanyan, Sonal Gupta, and Luke Zettlemoyer. 2021 · 2021
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On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al. 2021 · 2021
Earlier work this paper cites.
Federated learning and privacy: Building privacy-preserving systems for machine learning and data science on decentralized data
Kallista Bonawitz, Peter Kairouz, Brendan McMahan, and Daniel Ramage. 2021 · 2021
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Smart assistive architecture for the integration of iot devices, robotic systems, and multimodal interfaces in healthcare environments
Alberto Brunete, Ernesto Gambao, Miguel Hernando, and Raquel Cedazo. 2021 · 2021
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Fltrust: Byzantine-robust federated learning via trust bootstrapping
Xiaoyu Cao, Minghong Fang, Jia Liu, and Neil Zhenqiang Gong. 2021 · 2021
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Hetero{fl}: Computation and communication efficient federated learning for heterogeneous clients
Enmao Diao, Jie Ding, and Vahid Tarokh. 2021 · 2021
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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, Jakob Uszkoreit, and Neil Houlsby. 2021 · 2021
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Advances and open problems in federated learning
Peter Kairouz et al. 2021 · 2021
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
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Lotteryfl: Empower edge intelligence with personalized and communication-efficient federated learning
Ang Li, Jingwei Sun, Binghui Wang, Lin Duan, Sicheng Li, Yiran Chen, and Hai Li. 2021a · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
Cited alongside, same era.
Communication-efficient decentralized zeroth-order method on heterogeneous data
Zan Li and Li Chen. 2021 · 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 · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021 · 2021
Cited alongside, same era.
Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. 2021 · 2021
Cited alongside, same era.
Adaptive federated optimization
Efficient federated prompt tuning for black-box large pre-trained models
Zihao Lin, Yan Sun, Yifan Shi, Xueqian Wang, Lifu Huang, Li Shen, and Dacheng Tao. 2023 · 2023
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Communication efficient federated learning for multilingual neural machine translation with adapter
Yi Liu, Xiaohan Bi, Lei Li, Sishuo Chen, Wenkai Yang, and Xu Sun. 2023d · 2023
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Reducing communication overhead in federated learning for pre-trained language models using parameter-efficient finetuning
Shubham Malaviya, Manish Shukla, and Sachin Lodha. 2023 · 2023
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Federated multilingual models for medical transcript analysis
Andrea Manoel, Mirian del Carmen Hipolito Garcia, Tal Baumel, Shize Su, Jialei Chen, Robert Sim, Dan Miller, Danny Karmon, and Dimitrios Dimitriadis. 2023 · 2023
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Fedzen: Towards superlinear zeroth-order federated learning via incremental hessian estimation
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Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečný, Sanjiv Kumar, and Hugh Brendan McMahan. 2021 · 2021
Cited alongside, same era.
Communication-efficient and personalized federated lottery ticket learning
Sejin Seo, Seung-Woo Ko, Jihong Park, Seong-Lyun Kim, and Mehdi Bennis. 2021 · 2021
Cited alongside, same era.
Backdoor pre-trained models can transfer to all
Lujia Shen, Shouling Ji, Xuhong Zhang, Jinfeng Li, Jing Chen, Jie Shi, Chengfang Fang, Jianwei Yin, and Ting Wang. 2021 · 2021
Cited alongside, same era.
Waffle: Watermarking in federated learning
Buse G. A. Tekgul, Yuxi Xia, Samuel Marchal, and N. Asokan. 2021 · 2021
Cited alongside, same era.
BitFit: Simple parameter-efficient fine-tuning for transformer-based masked language-models
Elad Ben Zaken, Yoav Goldberg, and Shauli Ravfogel. 2022 · 2022
Cited alongside, same era.
Differentially private bias-term only fine-tuning of foundation models
Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, and George Karypis. 2022 · 2022
Cited alongside, same era.
Communication-efficient stochastic zeroth-order optimization for federated learning
Wenzhi Fang, Ziyi Yu, Yuning Jiang, Yuanming Shi, Colin N. Jones, and Yong Zhou. 2022 · 2022
Cited alongside, same era.
Alessio Maritan, Subhrakanti Dey, and Luca Schenato. 2023 · 2023
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Federated learning for smart cities: A comprehensive survey
Sharnil Pandya, Gautam Srivastava, Rutvij Jhaveri, M. Rajasekhara Babu, Sweta Bhattacharya, Praveen Kumar Reddy Maddikunta, Spyridon Mastorakis, Md. Jalil Piran, and Thippa Reddy Gadekallu. 2023 · 2023
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Robust speech recognition via large-scale weak supervision
Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine Mcleavey, and Ilya Sutskever. 2023 · 2023
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Survey on federated learning threats: Concepts, taxonomy on attacks and defences, experimental study and challenges
Nuria Rodríguez-Barroso, Daniel Jiménez-López, M Victoria Luzón, Francisco Herrera, and Eugenio Martínez-Cámara. 2023 · 2023
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Model compression for communication efficient federated learning
Suhail Mohmad Shah and Vincent K. N. Lau. 2023 · 2023
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FedTherapist: Mental health monitoring with user-generated linguistic expressions on smartphones via federated learning
Jaemin Shin, Hyungjun Yoon, Seungjoo Lee, Sungjoon Park, Yunxin Liu, Jinho Choi, and Sung-Ju Lee. 2023a · 2023
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Shangchao Su, Bin Li, and Xiangyang Xue. 2023 · 2023
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Fedperfix: Towards partial model personalization of vision transformers in federated learning
Guangyu Sun, Matias Mendieta, Jun Luo, Shandong Wu, and Chen Chen. 2023 · 2023
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Does synthetic data generation of llms help clinical text mining?
Ruixiang Tang, Xiaotian Han, Xiaoqian Jiang, and Xia Hu. 2023 · 2023
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Federated fine-tuning of foundation models via probabilistic masking
Vasileios Tsouvalas, Yuki Asano, and Aaqib Saeed. 2023 · 2023
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Alpaca-lora
Eric Wang. 2023 · 2023
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Fedbfpt: An efficient federated learning framework for bert further pre-training
Xin’ao Wang, Huan Li, Ke Chen, and Lidan Shou. 2023 · 2023
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Federatedscope: A flexible federated learning platform for heterogeneity
Yuexiang Xie, Zhen Wang, Dawei Gao, Daoyuan Chen, Liuyi Yao, Weirui Kuang, Yaliang Li, Bolin Ding, and Jingren Zhou. 2023 · 2023
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Federated learning of gboard language models with differential privacy
Zheng Xu, Yanxiang Zhang, Galen Andrew, Christopher Choquette, Peter Kairouz, Brendan Mcmahan, Jesse Rosenstock, and Yuanbo Zhang. 2023 · 2023
Later among the works it cites.
Efficient model personalization in federated learning via client-specific prompt generation
Fu-En Yang, Chien-Yi Wang, and Yu-Chiang Frank Wang. 2023a · 2023
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Who leaked the model? tracking IP infringers in accountable federated learning
Shuyang Yu, Junyuan Hong, Yi Zeng, Fei Wang, Ruoxi Jia, and Jiayu Zhou. 2023b · 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. 2023f · 2023
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A survey of large language models
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al. 2023 · 2023
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Reducing communication for split learning by randomized top-k sparsification
Fei Zheng, Chaochao Chen, Lingjuan Lyu, and Binhui Yao. 2023 · 2023
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Generalizable multilingual hate speech detection on low resource indian languages using fair selection in federated learning
Singh Akshay and Thakur Rahul. 2024 · 2024
Closest in time.
Feddat: An approach for foundation model finetuning in multi-modal heterogeneous federated learning
Haokun Chen, Yao Zhang, Denis Krompass, Jindong Gu, and Volker Tresp. 2024 · 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 · 2024
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Only send what you need: Learning to communicate efficiently in federated multilingual machine translation
Yun-Wei Chu, Dong-Jun Han, and Christopher G. Brinton. 2024 · 2024
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Unlocking the potential of prompt-tuning in bridging generalized and personalized federated learning
Wenlong Deng, Christos Thrampoulidis, and Xiaoxiao Li. 2024 · 2024
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Communication-efficient personalized federated learning for speech-to-text tasks
Yichao Du, Zhirui Zhang, Linan Yue, Xu Huang, Yuqing Zhang, Tong Xu, Linli Xu, and Enhong Chen. 2024 · 2024
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Prompt-enhanced federated content representation learning for cross-domain recommendation
Lei Guo, Ziang Lu, Junliang Yu, Quoc Viet Hung Nguyen, and Hongzhi Yin. 2024a · 2024
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FedLFC: Towards efficient federated multilingual modeling with LoRA-based language family clustering
Zhihan Guo, Yifei Zhang, Zhuo Zhang, Zenglin Xu, and Irwin King. 2024b · 2024
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Federated learning-empowered ai-generated content in wireless networks
Xumin Huang, Peichun Li, Hongyang Du, Jiawen Kang, Dusit Niyato, Dong In Kim, and Yuan Wu. 2024 · 2024
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Chatgpt makes medicine easy to swallow: an exploratory case study on simplified radiology reports
Katharina Jeblick, Balthasar Schachtner, Jakob Dexl, Andreas Mittermeier, Anna Theresa Stüber, Johanna Topalis, Tobias Weber, Philipp Wesp, Bastian Oliver Sabel, Jens Ricke, et al. 2024 · 2024
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Grounding foundation models through federated transfer learning: A general framework
Yan Kang, Tao Fan, Hanlin Gu, Xiaojin Zhang, Lixin Fan, and Qiang Yang. 2024 · 2024
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Blades: A unified benchmark suite for byzantine attacks and defenses in federated learning
Shenghui Li, Edith Ngai, Fanghua Ye, Li Ju, Tianru Zhang, and Thiemo Voigt. 2024b · 2024
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Xi Li and Jiaqi Wang. 2024 · 2024
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On the convergence of zeroth-order federated tuning in large language models
Zhenqing Ling, Daoyuan Chen, Liuyi Yao, Yaliang Li, and Ying Shen. 2024 · 2024
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Flora: Enhancing vision-language models with parameter-efficient federated learning
Duy Phuong Nguyen, J. Pablo Munoz, and Ali Jannesari. 2024 · 2024
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OpenAI. 2024 · 2024
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Evaluating llm – generated multimodal diagnosis from medical images and symptom analysis
Dimitrios P. Panagoulias, Maria Virvou, and George A. Tsihrintzis. 2024 · 2024
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FL-TAC: Enhanced fine-tuning in federated learning via low-rank, task-specific adapter clustering
Siqi Ping, Yuzhu Mao, Yang Liu, Xiao-Ping Zhang, and Wenbo Ding. 2024 · 2024
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Federated full-parameter tuning of billion-sized language models with communication cost under 18 kilobytes
Zhen Qin, Daoyuan Chen, Bingchen Qian, Bolin Ding, Yaliang Li, and Shuiguang Deng. 2024 · 2024
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Federated text-driven prompt generation for vision-language models
Chen Qiu, Xingyu Li, Chaithanya Kumar Mummadi, Madan Ravi Ganesh, Zhenzhen Li, Lu Peng, and Wan-Yi Lin. 2024 · 2024
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Advances and open challenges in federated learning with foundation models
Chao Ren, Han Yu, Hongyi Peng, Xiaoli Tang, Anran Li, Yulan Gao, Alysa Ziying Tan, Bo Zhao, Xiaoxiao Li, Zengxiang Li, and Qiang Yang. 2024 · 2024
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Empowering federated learning for massive models with nvidia flare
Holger R. Roth, Ziyue Xu, Yuan-Ting Hsieh, Adithya Renduchintala, Isaac Yang, Zhihong Zhang, Yuhong Wen, Sean Yang, Kevin Lu, Kristopher Kersten, Camir Ricketts, Daguang Xu, Chester Chen, Yan Cheng, and Andrew Feng. 2024 · 2024
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Personalized federated learning for text classification with gradient-free prompt tuning
Wang Rui, Yu Tong, Zhang Ruiyi, Kim Sungchul, Rossi Ryan A., Zhao Handong, Wu Junda, Mitra Subrata, Yao Lina, and Henao Ricardo. 2024 · 2024
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Federated adaptive prompt tuning for multi-domain collaborative learning
Shangchao Su, Mingzhao Yang, Bin Li, and Xiangyang Xue. 2024 · 2024
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Fedselect: Customized selection of parameters for fine-tuning during personalized federated learning
Rishub Tamirisa, John Won, Chengjun Lu, Ron Arel, and Andy Zhou. 2024 · 2024
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Analysis of privacy leakage in federated large language models
Minh Vu, Truc Nguyen, Tre’ Jeter, and My T. Thai. 2024 · 2024
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A survey on efficient federated learning methods for foundation model training
Herbert Woisetschläger, Alexander Isenko, Shiqiang Wang, Ruben Mayer, and Hans-Arno Jacobsen. 2024 · 2024
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Openfedllm: Training large language models on decentralized private data via federated learning
Rui Ye, Wenhao Wang, Jingyi Chai, Dihan Li, Zexi Li, Yinda Xu, Yaxin Du, Yanfeng Wang, and Siheng Chen. 2024 · 2024
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Federated recommendation via hybrid retrieval augmented generation
Huimin Zeng, Zhenrui Yue, Qian Jiang, and Dong Wang. 2024 · 2024
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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. 2024b · 2024
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Revisiting data reconstruction attacks on real-world dataset for federated natural language understanding
Zhuo Zhang, Jintao Huang, Xiangjing Hu, Jingyuan Zhang, Yating Zhang, Hui Wang, Yue Yu, Qifan Wang, Lizhen Qu, and Zenglin Xu. 2024c · 2024
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Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization
Amirhossein Reisizadeh, Aryan Mokhtari, Hamed Hassani, Ali Jadbabaie, and Ramtin Pedarsani. 2020 · 2031
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