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Federated learning (FL) enables multiple clients to collaboratively train a global model without disclosing their data.
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Communication-efficient learning of deep networks from decentralized data
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Attention is all you need
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A simple framework for contrastive learning of visual representations
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Personalized Federated Learning with Moreau Envelopes
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Lower bounds and optimal algorithms for personalized federated learning
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Federated visual classification with real-world data distribution
Hsu, T.-M. H.; Qi, H.; and Brown, M. 2020 · 2020
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SCAFFOLD: Stochastic controlled averaging for federated learning
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Li, T.; Sahu, A. K.; Zaheer, M.; Sanjabi, M.; Talwalkar, A.; and Smith, V. 2020 · 2020
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Fedvision: An online visual object detection platform powered by federated learning
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Model fusion via optimal transport
Singh, S. P.; and Jaggi, M. 2020 · 2020
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Federated learning with only positive labels
Yu, F.; Rawat, A. S.; Menon, A.; and Kumar, S. 2020 · 2020
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Performance optimization of federated person re-identification via benchmark analysis
Zhuang, W.; Wen, Y.; Zhang, X.; Gan, X.; Yin, D.; Zhou, D.; Zhang, S.; and Yi, S. 2020 · 2020
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Scaling up visual and vision-language representation learning with noisy text supervision
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MedCLIP: Contrastive Learning from Unpaired Medical Images and Text
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Attracting and dispersing: A simple approach for source-free domain adaptation
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DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detection
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Joint optimization in edge-cloud continuum for federated unsupervised person re-identification
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On pre-training for federated learning
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Towards Instance-adaptive Inference for Federated Learning
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Text-driven Prompt Generation for Vision-Language Models in Federated Learning
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Geodesic flow kernel for unsupervised domain adaptation
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