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Existing approaches in Federated Learning (FL) mainly focus on sending model parameters or gradients from clients to a server.
The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
Earlier work this paper cites.
3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
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ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Yfcc100m: The new data in multimedia research
Bart Thomee, David A Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, and Li-Jia Li · 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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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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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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Federated learning with non-iid data
Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra · 2018
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Neel Guha, Ameet Talwalkar, and Virginia Smith · 2019
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Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth · 2019
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A smaller subset of 10 easily classified classes from imagenet, and a little more french, 2019
Jeremy Howard · 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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Deep leakage from gradients
Ligeng Zhu, Zhijian Liu, and Song Han · 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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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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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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Adaptive federated optimization
Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečnỳ, Sanjiv Kumar, and H Brendan McMahan · 2020
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Tackling the objective inconsistency problem in heterogeneous federated optimization
Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, and H Vincent Poor · 2020
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Conceptual 12M: Pushing web-scale image-text pre-training to recognize long-tail visual concepts
Soravit Changpinyo, Piyush Sharma, Nan Ding, and Radu Soricut · 2021
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Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2021
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Preservation of the global knowledge by not-true distillation in federated learning
Gihun Lee, Minchan Jeong, Yongjin Shin, Sangmin Bae, and Se-Young Yun · 2021
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Model-contrastive federated learning
Qinbin Li, Bingsheng He, and Dawn Song · 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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A survey on security and privacy of federated learning
Viraaji Mothukuri, Reza M Parizi, Seyedamin Pouriyeh, Yan Huang, Ali Dehghantanha, and Gautam Srivastava · 2021
Cited alongside, same era.
Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen · 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
Cited alongside, same era.
On memorization in probabilistic deep generative models
Gerrit van den Burg and Chris Williams · 2021
Cited alongside, same era.
Membership inference attacks from first principles
Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, and Florian Tramer · 2022
Cited alongside, same era.
Dense: Data-free one-shot federated learning
Jie Zhang, Chen Chen, Bo Li, Lingjuan Lyu, Shuang Wu, Shouhong Ding, Chunhua Shen, and Chao Wu · 2022
Later among the works it cites.
Federated learning with label distribution skew via logits calibration
Jie Zhang, Zhiqi Li, Bo Li, Jianghe Xu, Shuang Wu, Shouhong Ding, and Chao Wu · 2022
Later among the works it cites.
Aligning before aggregating: Enabling cross-domain federated learning via consistent feature extraction
Guogang Zhu, Xuefeng Liu, Shaojie Tang, and Jianwei Niu · 2022
Later among the works it cites.
Dual cross-attention learning for fine-grained visual categorization and object re-identification
Haowei Zhu, Wenjing Ke, Dong Li, Ji Liu, Lu Tian, and Yi Shan · 2022
Later among the works it cites.
Align your latents: High-resolution video synthesis with latent diffusion models
Andreas Blattmann, Robin Rombach, Huan Ling, Tim Dockhorn, Seung Wook Kim, Sanja Fidler, and Karsten Kreis · 2023
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Dataset distillation by matching training trajectories
George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A Efros, and Jun-Yan Zhu · 2022
Cited alongside, same era.
Metaformer: A unified meta framework for fine-grained recognition
Qishuai Diao, Yi Jiang, Bin Wen, Jia Sun, and Zehuan Yuan · 2022
Cited alongside, same era.
Preserving privacy in federated learning with ensemble cross-domain knowledge distillation
Xuan Gong, Abhishek Sharma, Srikrishna Karanam, Ziyan Wu, Terrence Chen, David Doermann, and Arun Innanje · 2022
Cited alongside, same era.
Tao Guo, Song Guo, Junxiao Wang, and Wenchao Xu · 2022
Cited alongside, same era.
Is synthetic data from generative models ready for image recognition?
Ruifei He, Shuyang Sun, Xin Yu, Chuhui Xue, Wenqing Zhang, Philip Torr, Song Bai, and Xiaojuan Qi · 2022
Cited alongside, same era.
Cross-silo federated learning: Challenges and opportunities
Chao Huang, Jianwei Huang, and Xin Liu · 2022
Cited alongside, same era.
Zero-shot voice conditioning for denoising diffusion tts models
Alon Levkovitch, Eliya Nachmani, and Lior Wolf · 2022
Cited alongside, same era.
Nicholas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramer, Borja Balle, Daphne Ippolito, and Eric Wallace · 2023
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Image captions are natural prompts for text-to-image models
Shiye Lei, Hao Chen, Sen Zhang, Bo Zhao, and Dacheng Tao · 2023
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Effective passive membership inference attacks in federated learning against overparameterized models
Jiacheng Li, Ninghui Li, and Bruno Ribeiro · 2023
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Model extraction attacks on split federated learning
Jingtao Li, Adnan Siraj Rakin, Xing Chen, Li Yang, Zhezhi He, Deliang Fan, and Chaitali Chakrabarti · 2023
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Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi · 2023
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Is synthetic data from diffusion models ready for knowledge distillation?
Zheng Li, Yuxuan Li, Penghai Zhao, Renjie Song, Xiang Li, and Jian Yang · 2023
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Diffvoice: Text-to-speech with latent diffusion
Zhijun Liu, Yiwei Guo, and Kai Yu · 2023
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Scalable diffusion models with transformers
William Peebles and Saining Xie · 2023
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Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation
Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman · 2023
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Diffusion art or digital forgery? investigating data replication in diffusion models
Gowthami Somepalli, Vasu Singla, Micah Goldblum, Jonas Geiping, and Tom Goldstein · 2023
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Understanding and mitigating copying in diffusion models
Gowthami Somepalli, Vasu Singla, Micah Goldblum, Jonas Geiping, and Tom Goldstein · 2023
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Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification
Jiancheng Yang, Rui Shi, Donglai Wei, Zequan Liu, Lin Zhao, Bilian Ke, Hanspeter Pfister, and Bingbing Ni · 2023
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Exploring one-shot semi-supervised federated learning with a pre-trained diffusion model
Mingzhao Yang, Shangchao Su, Bin Li, and Xiangyang Xue · 2023
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Federated foundation models: Privacy-preserving and collaborative learning for large models
Sixing Yu, J Pablo Muñoz, and Ali Jannesari · 2023
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Forget-me-not: Learning to forget in text-to-image diffusion models
Eric Zhang, Kai Wang, Xingqian Xu, Zhangyang Wang, and Humphrey Shi · 2023
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Delving into the adversarial robustness of federated learning
Jie Zhang, Bo Li, Chen Chen, Lingjuan Lyu, Shuang Wu, Shouhong Ding, and Chao Wu · 2023
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Training on thin air: Improve image classification with generated data
Yongchao Zhou, Hshmat Sahak, and Jimmy Ba · 2023
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