Low-resolution data improves high-resolution model performance when high-resolution samples are limited, via KL-divergence bounds and experiments on vision transformers and CNNs.
arXiv preprint arXiv:2106.06042 , year=
6 Pith papers cite this work, alongside 48 external citations. Polarity classification is still indexing.
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A new framework trains personal digital health models using adaptive weights on support users including dissimilar ones, achieving up to 25% lower RMSE in low-data settings.
FedFrozen improves stability in heterogeneous federated Transformer training by warming up the full model then freezing the attention kernel (query/key) while optimizing the value block under a fixed kernel.
FedHD is a federated learning framework for whole slide images that distills one-to-one synthetic features aligned via Gaussian mixtures and progressively integrates cross-site features through curriculum learning to handle institutional heterogeneity.
FedKPer improves the generalization-personalization trade-off in medical federated learning via local knowledge personalization and selective aggregation that emphasizes reliable updates.
Empirical comparison shows APPLE, FedGC, and FedProto outperform other PFL algorithms on MNIST, SignMNIST, and Digit5 using accuracy, precision, recall, and F1 score.
citing papers explorer
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On What We Can Learn from Low-Resolution Data
Low-resolution data improves high-resolution model performance when high-resolution samples are limited, via KL-divergence bounds and experiments on vision transformers and CNNs.
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Personalized Digital Health Modeling with Adaptive Support Users
A new framework trains personal digital health models using adaptive weights on support users including dissimilar ones, achieving up to 25% lower RMSE in low-data settings.
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FedFrozen: Two-Stage Federated Optimization via Attention Kernel Freezing
FedFrozen improves stability in heterogeneous federated Transformer training by warming up the full model then freezing the attention kernel (query/key) while optimizing the value block under a fixed kernel.
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Federated Distillation for Whole Slide Image via Gaussian-Mixture Feature Alignment and Curriculum Integration
FedHD is a federated learning framework for whole slide images that distills one-to-one synthetic features aligned via Gaussian mixtures and progressively integrates cross-site features through curriculum learning to handle institutional heterogeneity.
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FedKPer: Tackling Generalization and Personalization in Medical Federated Learning via Knowledge Personalization
FedKPer improves the generalization-personalization trade-off in medical federated learning via local knowledge personalization and selective aggregation that emphasizes reliable updates.
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Pattern Recognition Tasks with Personalized Federated Learning
Empirical comparison shows APPLE, FedGC, and FedProto outperform other PFL algorithms on MNIST, SignMNIST, and Digit5 using accuracy, precision, recall, and F1 score.