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In this work, we propose GPT-FL, a generative pre-trained model-assisted federated learning (FL) framework.
Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Invertible bloom lookup tables
Michael T. Goodrich and Michael Mitzenmacher · 2011
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Food-101–mining discriminative components with random forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Deep residual learning for image recognition
Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun · 2015
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Practical secure aggregation for federated learning on user-held data
K. A. Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2016
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Communication-efficient learning of deep networks from decentralized data
H. B. McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas · 2016
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Practical secure aggregation for privacy-preserving machine learning
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. B. McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Training neural speech recognition systems with synthetic speech augmentation
Jason Li, Ravi Gadde, Boris Ginsburg, and Vitaly Lavrukhin · 2018
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Federated optimization in heterogeneous networks
Anit Kumar Sahu, Tian Li, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, and Virginia Smith · 2018
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Speech commands: A dataset for limited-vocabulary speech recognition
Pete Warden · 2018
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Gradient diversity: a key ingredient for scalable distributed learning
Dong Yin, Ashwin Pananjady, Max Lam, Dimitris Papailiopoulos, Kannan Ramchandran, and Peter Bartlett · 2018
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Towards federated learning at scale: System design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloé Kiddon, Jakub Konecný, Stefano Mazzocchi, H. B. McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, and Jason Roselander · 2019
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On biased stochastic gradient estimation
Derek Driggs, Jingwei Liang, and Carola-Bibiane Schönlieb · 2019
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Scaffold: Stochastic controlled averaging for federated learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, and Ananda Theertha Suresh · 2019
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Meld: A multimodal multi-party dataset for emotion recognition in conversations
Soujanya Poria, Devamanyu Hazarika, Navonil Majumder, Gautam Naik, Erik Cambria, and Rada Mihalcea · 2019
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On the convergence of sgd with biased gradients
Ahmad Ajalloeian and Sebastian U. Stich · 2020
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Can ai help in screening viral and covid-19 pneumonia?
Muhammad EH Chowdhury, Tawsifur Rahman, Amith Khandakar, Rashid Mazhar, Muhammad Abdul Kadir, Zaid Bin Mahbub, Khandakar Reajul Islam, Muhammad Salman Khan, Atif Iqbal, Nasser Al Emadi, et al · 2020
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Distillation-based semi-supervised federated learning for communication-efficient collaborative training with non-iid private data
Sohei Itahara, Takayuki Nishio, Yusuke Koda, Masahiro Morikura, and Koji Yamamoto · 2020
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Ensemble distillation for robust model fusion in federated learning
Tao Lin, Lingjing Kong, Sebastian U. Stich, and Martin Jaggi · 2020
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Adaptive federated optimization
Sashank J. Reddi, Zachary B. Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konecný, Sanjiv Kumar, and H. B. McMahan · 2020
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Attack of the tails: Yes, you really can backdoor federated learning
Hongyi Wang, Kartik K. Sreenivasan, Shashank Rajput, Harit Vishwakarma, Saurabh Agarwal, Jy yong Sohn, Kangwook Lee, and Dimitris Papailiopoulos · 2020
Cited alongside, same era.
Dataset condensation via efficient synthetic-data parameterization
Jang-Hyun Kim, Jinuk Kim, Seong Joon Oh, Sangdoo Yun, Hwanjun Song, Joonhyun Jeong, Jung-Woo Ha, and Hyun Oh Song · 2022
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Medisecfed: Private and secure medical image classification in the presence of malicious clients
Abhinav Kumar, Vishal Purohit, Vandana Bharti, Rishav Singh, and Sanjay Kumar Singh · 2022
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Variance reduced proxskip: Algorithm, theory and application to federated learning
Grigory Malinovsky, Kai Yi, and Peter Richt’arik · 2022
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Proxskip: Yes! local gradient steps provably lead to communication acceleration! finally!
Konstantin Mishchenko, Grigory Malinovsky, Sebastian U. Stich, and Peter Richt’arik · 2022
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Where to begin? on the impact of pre-training and initialization in federated learning
John Nguyen, Jianyu Wang, Kshitiz Malik, Maziar Sanjabi, and Michael G. Rabbat · 2022
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Speecht5: Unified-modal encoder-decoder pre-training for spoken language processing
Junyi Ao, Rui Wang, Long Zhou, Shujie Liu, Shuo Ren, Yu Wu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, and Furu Wei · 2021
Cited alongside, same era.
A method to reveal speaker identity in distributed asr training, and how to counter it
Trung Dang, Om Thakkar, Swaroop Indra Ramaswamy, Rajiv Mathews, Peter Chin, and Franccoise Beaufays · 2021
Cited alongside, same era.
Evaluating gradient inversion attacks and defenses in federated learning
Yangsibo Huang, Samyak Gupta, Zhao Song, Kai Li, and Sanjeev Arora · 2021
Cited alongside, same era.
Nonlinear programming
James Killen · 2021
Cited alongside, same era.
Model-contrastive federated learning
Qinbin Li, Bingsheng He, and Dawn Xiaodong Song · 2021
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
Robin Rombach, A. Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2021
Cited alongside, same era.
Lightsecagg: a lightweight and versatile design for secure aggregation in federated learning
Jinhyun So, Chaoyang He, Chien-Sheng Yang, Songze Li, Qian Yu, Ramy E. Ali, Basak Guler, and Salman Avestimehr · 2021
Cited alongside, same era.
Does knowledge distillation really work?
Samuel Stanton, Pavel Izmailov, P. Kirichenko, Alexander A. Alemi, and Andrew Gordon Wilson · 2021
Cited alongside, same era.
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Dynafed: Tackling client data heterogeneity with global dynamics
Renjie Pi, Weizhong Zhang, Yueqi Xie, Jiahui Gao, Xiaoyu Wang, Sunghun Kim, and Qifeng Chen · 2022
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Laion-5b: An open large-scale dataset for training next generation image-text models
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, Patrick Schramowski, Srivatsa Kundurthy, Katherine Crowson, Ludwig Schmidt, Robert Kaczmarczyk, and Jenia Jitsev · 2022
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Fine-tuning global model via data-free knowledge distillation for non-iid federated learning
Lin Zhang, Li Shen, Liang Ding, Dacheng Tao, and Ling-Yu Duan · 2022
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Fedmultimodal: A benchmark for multimodal federated learning
Tiantian Feng, Digbalay Bose, Tuo Zhang, Rajat Hebbar, Anil Ramakrishna, Rahul Gupta, Mi Zhang, Salman Avestimehr, and Shrikanth S. Narayanan · 2023
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Federated heavy hitter recovery under linear sketching
Adrià Gascón, Peter Kairouz, Ziteng Sun, and Ananda Theertha Suresh · 2023
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Gemini: A family of highly capable multimodal models
Google Gemini Team · 2023
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Privately customizing prefinetuning to better match user data in federated learning
Charlie Hou, Hongyuan Zhan, Akshat Shrivastava, Sida I. Wang, Sasha Livshits, Giulia C. Fanti, and Daniel Lazar · 2023
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Audioldm: Text-to-audio generation with latent diffusion models
Haohe Liu, Zehua Chen, Yiitan Yuan, Xinhao Mei, Xubo Liu, Danilo P. Mandic, Wenwu Wang, and MarkD . Plumbley · 2023
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Diversity is definitely needed: Improving model-agnostic zero-shot classification via stable diffusion
Jordan Shipard, Arnold Wiliem, Kien Nguyen Thanh, Wei Xiang, and Clinton Fookes · 2023
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Understanding generalization of federated learning via stability: Heterogeneity matters
Zhenyu Sun, Xiaochun Niu, and Ermin Wei · 2023
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Private heavy hitters, 2023
TensorFlow · 2023
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Can public large language models help private cross-device federated learning?
Boxin Wang, Yibo Zhang, Yuan Cao, Bo Li, H. B. McMahan, Sewoong Oh, Zheng Xu, and Manzil Zaheer · 2023
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Achintha Wijesinghe, Songyang Zhang, and Zhi Ding · 2023
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Fedaudio: A federated learning benchmark for audio tasks
Tuo Zhang, Tiantian Feng, Samiul Alam, Sunwoo Lee, Mi Zhang, Shrikanth S. Narayanan, and Salman Avestimehr · 2023
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Improved generalization bounds for communication efficient federated learning
Peyman Gholami and Hulya Seferoglu · 2024
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