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Federated learning (FL) provides a decentralized machine learning paradigm where a server collaborates with a group of clients to learn a global model without accessing the clients' data.
Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification
Hsu, T. H.; Qi, H.; and Brown, M. 2019 · 1909
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Learning multiple layers of features from tiny images
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Kullback-Leibler Divergence
Joyce, J. M. 2011 · 2011
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Fed-Focal Loss for imbalanced data classification in Federated Learning
Sarkar, D.; Narang, A.; and Rai, S. 2020 · 2011
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Imagenet large scale visual recognition challenge
Russakovsky, O.; Deng, J.; Su, H.; Krause, J.; Satheesh, S.; Ma, S.; Huang, Z.; Karpathy, A.; Khosla, A.; Bernstein, M.; et al. 2015 · 2015
Earlier work this paper cites.
Privacy-Preserving Deep Learning
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Communication-efficient learning of deep networks from decentralized data
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Earlier work this paper cites.
Class-balanced loss based on effective number of samples
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Earlier work this paper cites.
Similarity of neural network representations revisited
Kornblith, S.; Norouzi, M.; Lee, H.; and Hinton, G. 2019 · 2019
Earlier work this paper cites.
FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare
Chen, Y.; Qin, X.; Wang, J.; Yu, C.; and Gao, W. 2020 · 2020
Earlier work this paper cites.
Self-balancing federated learning with global imbalanced data in mobile systems
Duan, M.; Liu, D.; Chen, X.; Liu, R.; Tan, Y.; and Liang, L. 2020 · 2020
Earlier work this paper cites.
Decoupling Representation and Classifier for Long-Tailed Recognition
Kang, B.; Xie, S.; Rohrbach, M.; Yan, Z.; Gordo, A.; Feng, J.; and Kalantidis, Y. 2020 · 2020
Earlier work this paper cites.
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
Karimireddy, S. P.; Kale, S.; Mohri, M.; Reddi, S. J.; Stich, S. U.; and Suresh, A. T. 2020 · 2020
Earlier work this paper cites.
Federated optimization in heterogeneous networks
Li, T.; Sahu, A. K.; Zaheer, M.; Sanjabi, M.; Talwalkar, A.; and Smith, V. 2020 · 2020
Earlier work this paper cites.
Ensemble Distillation for Robust Model Fusion in Federated Learning
Lin, T.; Kong, L.; Stich, S. U.; and Jaggi, M. 2020 · 2020
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Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization
Wang, J.; Liu, Q.; Liang, H.; Joshi, G.; and Poor, H. V. 2020 · 2020
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FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning
Chen, H.; and Chao, W. 2021 · 2021
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Behavior Mimics Distribution: Combining Individual and Group Behaviors for Federated Learning
Huang, H.; Shang, F.; Liu, Y.; and Liu, H. 2021 · 2021
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FedBN: Federated Learning on Non-IID Features via Local Batch Normalization
Li, X.; Jiang, M.; Zhang, X.; Kamp, M.; and Dou, Q. 2021 · 2021
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No fear of heterogeneity: Classifier calibration for federated learning with non-iid data
Learn from others and be yourself in heterogeneous federated learning
Huang, W.; Ye, M.; and Du, B. 2022 · 2022
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Few-Shot Model Agnostic Federated Learning
Huang, W.; Ye, M.; Du, B.; and Gao, X. 2022 · 2022
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CLIP4Clip: An empirical study of CLIP for end to end video clip retrieval and captioning
Luo, H.; Ji, L.; Zhong, M.; Chen, Y.; Lei, W.; Duan, N.; and Li, T. 2022 · 2022
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High-Resolution Image Synthesis with Latent Diffusion Models
Rombach, R.; Blattmann, A.; Lorenz, D.; Esser, P.; and Ommer, B. 2022 · 2022
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Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Features
Shang, X.; Lu, Y.; Huang, G.; and Wang, H. 2022 · 2022
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How Much Can CLIP Benefit Vision-and-Language Tasks?
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Luo, M.; Chen, F.; Hu, D.; Zhang, Y.; Liang, J.; and Feng, J. 2021 · 2021
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Learning transferable visual models from natural language supervision
Radford, A.; Kim, J. W.; Hallacy, C.; Ramesh, A.; Goh, G.; Agarwal, S.; Sastry, G.; Askell, A.; Mishkin, P.; Clark, J.; et al. 2021 · 2021
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Addressing class imbalance in federated learning
Wang, L.; Xu, S.; Wang, X.; and Zhu, Q. 2021 · 2021
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ActionCLIP: A New Paradigm for Video Action Recognition
Wang, M.; Xing, J.; and Liu, Y. 2021 · 2021
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Federated learning with class imbalance reduction
Yang, M.; Wang, X.; Zhu, H.; Wang, H.; and Qian, H. 2021 · 2021
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Parameterized Knowledge Transfer for Personalized Federated Learning
Zhang, J.; Guo, S.; Ma, X.; Wang, H.; Xu, W.; and Wu, F. 2021 · 2021
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Dataset Condensation with Gradient Matching
Zhao, B.; Mopuri, K. R.; and Bilen, H. 2021 · 2021
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Shen, S.; Li, L. H.; Tan, H.; Bansal, M.; Rohrbach, A.; Chang, K.; Yao, Z.; and Keutzer, K. 2022 · 2022
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BalanceFL: Addressing class imbalance in long-tail federated learning
Shuai, X.; Shen, Y.; Jiang, S.; Zhao, Z.; Yan, Z.; and Xing, G. 2022 · 2022
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ZeroCap: Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic
Tewel, Y.; Shalev, Y.; Schwartz, I.; and Wolf, L. 2022 · 2022
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CLIPasso: semantically-aware object sketching
Vinker, Y.; Pajouheshgar, E.; Bo, J. Y.; Bachmann, R. C.; Bermano, A. H.; Cohen-Or, D.; Zamir, A.; and Shamir, A. 2022 · 2022
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GroupViT: Semantic Segmentation Emerges from Text Supervision
Xu, J.; Mello, S. D.; Liu, S.; Byeon, W.; Breuel, T. M.; Kautz, J.; and Wang, X. 2022 · 2022
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Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning
Zhang, L.; Shen, L.; Ding, L.; Tao, D.; and Duan, L. 2022 · 2022
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CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP
Chen, R.; Liu, Y.; Kong, L.; Zhu, X.; Ma, Y.; Li, Y.; Hou, Y.; Qiao, Y.; and Wang, W. 2023 · 2023
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On the effectiveness of partial variance reduction in federated learning with heterogeneous data
Li, B.; Schmidt, M. N.; Alstrøm, T. S.; and Stich, S. U. 2023 · 2023
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