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Previous studies on federated learning (FL) often encounter performance degradation due to data heterogeneity among different clients.
Bayesian learning via stochastic gradient langevin dynamics
Max Welling and Yee W Teh · 2011
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, Jeff Dean, et al · 2015
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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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Communication-efficient Learning of Deep Networks from Decentralized Data
H Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, et al · 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 optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2018
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Federated optimization for heterogeneous networks
Anit Kumar Sahu, Tian Li, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, and Virginia Smith · 2018
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Self-adversarially learned bayesian sampling, 2018
Yang Zhao, Jianyi Zhang, and Changyou Chen · 2018
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Class-balanced loss based on effective number of samples
Yin Cui, Menglin Jia, Tsung-Yi Lin, Yang Song, and Serge Belongie · 2019
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Measuring the effects of non-identical data distribution for federated visual classification
Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown · 2019
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Knowledge distillation via route constrained optimization
Xiao Jin, Baoyun Peng, Yichao Wu, Yu Liu, Jiaheng Liu, Ding Liang, Junjie Yan, and Xiaolin Hu · 2019
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Decoupling representation and classifier for long-tailed recognition
Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan, Albert Gordo, Jiashi Feng, and Yannis Kalantidis · 2019
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Stein variational gradient descent: A general purpose bayesian inference algorithm, 2019
Qiang Liu and Dilin Wang · 2019
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Large-scale long-tailed recognition in an open world
Ziwei Liu, Zhongqi Miao, Xiaohang Zhan, Jiayun Wang, Boqing Gong, and Stella X Yu · 2019
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Cyclical stochastic gradient mcmc for bayesian deep learning
Ruqi Zhang, Chunyuan Li, Jianyi Zhang, Changyou Chen, and Andrew Gordon Wilson · 2019
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Deep leakage from gradients
Ligeng Zhu, Zhijian Liu, and Song Han · 2019
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Fedbe: Making bayesian model ensemble applicable to federated learning
Hong-You Chen and Wei-Lun Chao · 2020
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Fedhealth: A federated transfer learning framework for wearable healthcare
Yiqiang Chen, Xin Qin, Jindong Wang, Chaohui Yu, and Wen Gao · 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
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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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Improved knowledge distillation via teacher assistant
Seyed Iman Mirzadeh, Mehrdad Farajtabar, Ang Li, Nir Levine, Akihiro Matsukawa, and Hassan Ghasemzadeh · 2020
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Fed-focal loss for imbalanced data classification in federated learning
Dipankar Sarkar, Ankur Narang, and Sumit Rai · 2020
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Feded: Federated learning via ensemble distillation for medical relation extraction
Dianbo Sui, Yubo Chen, Jun Zhao, Yantao Jia, Yuantao Xie, and Weijian Sun · 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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Addressing class imbalance in federated learning
Lixu Wang, Shichao Xu, Xiao Wang, and Qi Zhu · 2020
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Federated learning with class imbalance reduction, 2020
Miao Yang, Akitanoshou Wong, Hongbin Zhu, Haifeng Wang, and Hua Qian · 2020
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Stochastic particle-optimization sampling and the non-asymptotic convergence theory
Jianyi Zhang, Ruiyi Zhang, Lawrence Carin, and Changyou Chen · 2020
Cited alongside, same era.
Variance reduction in stochastic particle-optimization sampling
Jianyi Zhang, Yang Zhao, and Changyou Chen · 2020
Cited alongside, same era.
Towards fair federated learning with zero-shot data augmentation
Weituo Hao, Mostafa El-Khamy, Jungwon Lee, Jianyi Zhang, Kevin J Liang, Changyou Chen, and Lawrence Carin Duke · 2021
Cited alongside, same era.
Towards instance-adaptive inference for federated learning
Chun-Mei Feng, Kai Yu, Nian Liu, Xinxing Xu, Salman Khan, and Wangmeng Zuo · 2023
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Llama-adapter v2: Parameter-efficient visual instruction model
Peng Gao, Jiaming Han, Renrui Zhang, Ziyi Lin, Shijie Geng, Aojun Zhou, Wei Zhang, Pan Lu, Conghui He, Xiangyu Yue, et al · 2023
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Rethinking federated learning with domain shift: A prototype view
Wenke Huang, Mang Ye, Zekun Shi, He Li, and Bo Du · 2023
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Dacbert: Leveraging dependency agreement for cost-efficient bert pretraining
Martin Kuo, Jianyi Zhang, and Yiran Chen · 2023
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Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi · 2023
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Hua Huang, Fanhua Shang, Yuanyuan Liu, and Hongying Liu · 2021
Cited alongside, same era.
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
Cited alongside, same era.
No fear of heterogeneity: Classifier calibration for federated learning with non-iid data
Mi Luo, Fei Chen, Dapeng Hu, Yifan Zhang, Jian Liang, and Jiashi Feng · 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.
Flop: Federated learning on medical datasets using partial networks
Qian Yang, Jianyi Zhang, Weituo Hao, Gregory P. Spell, and Lawrence Carin · 2021
Cited alongside, same era.
Parameterized knowledge transfer for personalized federated learning
Jie Zhang, Song Guo, Xiaosong Ma, Haozhao Wang, Wenchao Xu, and Feijie Wu · 2021
Cited alongside, same era.
Why do we need large batchsizes in contrastive learning? a gradient-bias perspective
Changyou Chen, Jianyi Zhang, Yi Xu, Liqun Chen, Jiali Duan, Yiran Chen, Son Tran, Belinda Zeng, and Trishul Chilimbi · 2022
Cited alongside, same era.
Later among the works it cites.
Videochat: Chat-centric video understanding
KunChang Li, Yinan He, Yi Wang, Yizhuo Li, Wenhai Wang, Ping Luo, Yali Wang, Limin Wang, and Yu Qiao · 2023
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Visual instruction tuning, 2023
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee · 2023
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Video-chatgpt: Towards detailed video understanding via large vision and language models
Muhammad Maaz, Hanoona Rasheed, Salman Khan, and Fahad Shahbaz Khan · 2023
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OpenAI · 2023
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Clip-guided federated learning on heterogeneous and long-tailed data
Jiangming Shi, Shanshan Zheng, Xiangbo Yin, Yang Lu, Yuan Xie, and Yanyun Qu · 2023
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Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al · 2023
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Visual chatgpt: Talking, drawing and editing with visual foundation models
Chenfei Wu, Shengming Yin, Weizhen Qi, Xiaodong Wang, Zecheng Tang, and Nan Duan · 2023
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Learning federated visual prompt in null space for mri reconstruction
Chun-Mei Feng Bangjun Li Xinxing Xu, Yong Liu, and Huazhu Fu Wangmeng Zuo · 2023
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Mm-react: Prompting chatgpt for multimodal reasoning and action
Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Ehsan Azarnasab, Faisal Ahmed, Zicheng Liu, Ce Liu, Michael Zeng, and Lijuan Wang · 2023
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Fed-cbs: A heterogeneity-aware client sampling mechanism for federated learning via class-imbalance reduction
Jianyi Zhang, Ang Li, Minxue Tang, Jingwei Sun, Xiang Chen, Fan Zhang, Changyou Chen, Yiran Chen, and Hai Li · 2023
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Reaugkd: Retrieval-augmented knowledge distillation for pre-trained language models
Jianyi Zhang, Aashiq Muhamed, Aditya Anantharaman, Guoyin Wang, Changyou Chen, Kai Zhong, Qingjun Cui, Yi Xu, Belinda Zeng, Trishul Chilimbi, et al · 2023
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Minigpt-4: Enhancing vision-language understanding with advanced large language models
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny · 2023
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Sufficient context: A new lens on retrieval augmented generation systems
Hailey Joren, Jianyi Zhang, Chun-Sung Ferng, Da-Cheng Juan, Ankur Taly, and Cyrus Rashtchian · 2024
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Qinsi Wang, Saeed Vahidian, Hancheng Ye, Jianyang Gu, Jianyi Zhang, and Yiran Chen · 2024
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Sled: Self logits evolution decoding for improving factuality in large language models
Jianyi Zhang, Da-Cheng Juan, Cyrus Rashtchian, Chun-Sung Ferng, Heinrich Jiang, and Yiran Chen · 2024
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Min-k%++: Improved baseline for detecting pre-training data from large language models
Jingyang Zhang, Jingwei Sun, Eric Yeats, Yang Ouyang, Martin Kuo, Jianyi Zhang, Hao Frank Yang, and Hai Li · 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 · 2024
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Unlocking the potential of federated learning: The symphony of dataset distillation via deep generative latents
Yuqi Jia, Saeed Vahidian, Jingwei Sun, Jianyi Zhang, Vyacheslav Kungurtsev, Neil Zhenqiang Gong, and Yiran Chen · 2025
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