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When personalized federated learning (FL) meets large foundation models, new challenges arise from various limitations in resources.
Black-box models from input-output measurements
Lennart Ljung · 2001
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Stochastic first-and zeroth-order methods for nonconvex stochastic programming
Saeed Ghadimi and Guanghui Lan · 2013
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts · 2013
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Good debt or bad debt: Detecting semantic orientations in economic texts
P. Malo, A. Sinha, P. Korhonen, J. Wallenius, and P. Takala · 2014
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Trends in extreme learning machines: A review
Gao Huang, Guang-Bin Huang, Shiji Song, and Keyou You · 2015
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A comprehensive linear speedup analysis for asynchronous stochastic parallel optimization from zeroth-order to first-order
Xiangru Lian, Huan Zhang, Cho-Jui Hsieh, Yijun Huang, and Ji Liu · 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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The eu general data protection regulation (gdpr)
Paul Voigt and Axel Von dem Bussche · 2017
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Recognition in terra incognita
Sara Beery, Grant Van Horn, and Pietro Perona · 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 · 2018
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A survey of methods for explaining black box models
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi · 2018
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Zeroth-order stochastic variance reduction for nonconvex optimization
Sijia Liu, Bhavya Kailkhura, Pin-Yu Chen, Paishun Ting, Shiyu Chang, and Lisa Amini · 2018
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Stochastic zeroth-order optimization in high dimensions
Yining Wang, Simon Du, Sivaraman Balakrishnan, and Aarti Singh · 2018
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Aptos 2019 blindness detection, 2019
Sohier Dane Karthik, Maggie · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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Robust and communication-efficient federated learning from non-iid data
Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, and Wojciech Samek · 2019
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Autozoom: Autoencoder-based zeroth order optimization method for attacking black-box neural networks
Chun-Chen Tu, Paishun Ting, Pin-Yu Chen, Sijia Liu, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh, and Shin-Ming Cheng · 2019
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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 R Bowman · 2019
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Neural network acceptability judgments
Alex Warstadt, Amanpreet Singh, and Samuel R Bowman · 2019
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Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
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Bayesian nonparametric federated learning of neural networks
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Nghia Hoang, and Yasaman Khazaeni · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Albert: A lite bert for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut · 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
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Darknetz: towards model privacy at the edge using trusted execution environments
Fan Mo, Ali Shahin Shamsabadi, Kleomenis Katevas, Soteris Demetriou, Ilias Leontiadis, Andrea Cavallaro, and Hamed Haddadi · 2020
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Curated dataset for covid-19 posterior-anterior chest radiography images (x-rays)
Unais Sait, KG Lal, S Prajapati, Rahul Bhaumik, Tarun Kumar, S Sanjana, and Kriti Bhalla · 2020
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Transfer learning without knowing: Reprogramming black-box machine learning models with scarce data and limited resources
Yun-Yun Tsai, Pin-Yu Chen, and Tsung-Yi Ho · 2020
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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
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Deberta: Decoding-enhanced bert with disentangled attention
Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen · 2021
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Fedbn: Federated learning on non-iid features via local batch normalization
Xiaoxiao Li, Meirui JIANG, Xiaofei Zhang, Michael Kamp, and Qi Dou · 2021
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Communication-efficient decentralized zeroth-order method on heterogeneous data
Zan Li and Li Chen · 2021
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Reprogrammable-fl: Improving utility-privacy tradeoff in federated learning via model reprogramming
Huzaifa Arif, Alex Gittens, and Pin-Yu Chen · 2023
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A study on prompt design, advantages and limitations of chatgpt for deep learning program repair
Jialun Cao, Meiziniu Li, Ming Wen, and Shing-chi Cheung · 2023
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Less-vfl: Communication-efficient feature selection for vertical federated learning
Timothy Castiglia, Yi Zhou, Shiqiang Wang, Swanand Ravindra Kadhe, Nathalie Baracaldo Angel, and Stacy Patterson · 2023
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Parameter-efficient fine-tuning of large-scale 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 · 2023
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Does federated learning really need backpropagation?
Haozhe Feng, Tianyu Pang, Chao Du, Wei Chen, Shuicheng Yan, and Min Lin · 2023
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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
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Swarm learning for decentralized and confidential clinical machine learning
Stefanie Warnat-Herresthal, Hartmut Schultze, Krishnaprasad Lingadahalli Shastry, Sathyanarayanan Manamohan, Saikat Mukherjee, Vishesh Garg, Ravi Sarveswara, Kristian Händler, Peter Pickkers, N Ahmad Aziz, et al · 2021
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A survey on federated learning
Chen Zhang, Yu Xie, Hang Bai, Bin Yu, Weihong Li, and Yuan Gao · 2021
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Federated learning review: Fundamentals, enabling technologies, and future applications
Syreen Banabilah, Moayad Aloqaily, Eitaa Alsayed, Nida Malik, and Yaser Jararweh · 2022
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Fedadapter: Efficient federated learning for modern nlp
Dongqi Cai, Yaozong Wu, Shangguang Wang, Felix Xiaozhu Lin, and Mengwei Xu · 2022
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On bridging generic and personalized federated learning for image classification
Hong-You Chen and Wei-Lun Chao · 2022
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Model reprogramming: Resource-efficient cross-domain machine learning
Pin-Yu Chen · 2022
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Promptfl: Let federated participants cooperatively learn prompts instead of models-federated learning in age of foundation model
Tao Guo, Song Guo, Junxiao Wang, Xueyang Tang, and Wenchao Xu · 2023
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Test-time robust personalization for federated learning
Liangze Jiang and Tao Lin · 2023
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Weirui Kuang, Bingchen Qian, Zitao Li, Daoyuan Chen, Dawei Gao, Xuchen Pan, Yuexiang Xie, Yaliang Li, Bolin Ding, and Jingren Zhou · 2023
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Visual prompt based personalized federated learning
Guanghao Li, Wansen Wu, Yan Sun, Li Shen, Baoyuan Wu, and Dacheng Tao · 2023
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig · 2023
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Fedclip: Fast generalization and personalization for clip in federated learning
Wang Lu, HU Xixu, Jindong Wang, and Xing Xie · 2023
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Guilherme Penedo, Quentin Malartic, Daniel Hesslow, Ruxandra Cojocaru, Alessandro Cappelli, Hamza Alobeidli, Baptiste Pannier, Ebtesam Almazrouei, and Julien Launay · 2023
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Fedsampling: A better sampling strategy for federated learning
Tao Qi, Fangzhao Wu, Lingjuan Lyu, Yongfeng Huang, and Xing Xie · 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
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Perfedmask: Personalized federated learning with optimized masking vectors
Mehdi Setayesh, Xiaoxiao Li, and Vincent WS Wong · 2023
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Securing secure aggregation: Mitigating multi-round privacy leakage in federated learning
Jinhyun So, Ramy E Ali, Başak Güler, Jiantao Jiao, and A Salman Avestimehr · 2023
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Trustworthy federated learning: A survey
Asadullah Tariq, Mohamed Adel Serhani, Farag Sallabi, Tariq Qayyum, Ezedin S Barka, and Khaled A Shuaib · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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Flipkart product reviews with sentiment dataset, 2023
Nirali Vaghani and Mansi Thummar · 2023
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Chatgpt: five priorities for research
Eva AM Van Dis, Johan Bollen, Willem Zuidema, Robert van Rooij, and Claudi L Bockting · 2023
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Federated training of dual encoding models on small non-iid client datasets
Raviteja Vemulapalli, Warren Richard Morningstar, Philip Andrew Mansfield, Hubert Eichner, Karan Singhal, Arash Afkanpour, and Bradley Green · 2023
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A four-pronged defense against byzantine attacks in federated learning
Wei Wan, Shengshan Hu, Minghui Li, Jianrong Lu, Longling Zhang, Leo Yu Zhang, and Hai Jin · 2023
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A prompt pattern catalog to enhance prompt engineering with chatgpt
Jules White, Quchen Fu, Sam Hays, Michael Sandborn, Carlos Olea, Henry Gilbert, Ashraf Elnashar, Jesse Spencer-Smith, and Douglas C Schmidt · 2023
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Pengwei Xing, Songtao Lu, and Han Yu · 2023
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Eric Zelikman, Qian Huang, Percy Liang, Nick Haber, and Noah D Goodman · 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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