PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning | Paper Citation Record

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This paper cites Federated Learning with Personalization Layers.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Federated Learning with Personalization Layers

Reference 1

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This paper cites Federated learning with hierarchical clustering of local updates to improve training on non-iid data.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Federated learning with hierarchical clustering of local updates to improve training on non-iid data

Reference 2

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This paper cites LEAF: A Benchmark for Federated Settings.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning LEAF: A Benchmark for Federated Settings

Reference 3

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This paper cites Entropy-sgd: Biasing gradient descent into wide valleys.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Entropy-sgd: Biasing gradient descent into wide valleys

Reference 4

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This paper cites Shallowing deep networks: Layer-wise pruning based on feature representations.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Shallowing deep networks: Layer-wise pruning based on feature representations

Reference 5

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This paper cites Maximizing Global Model Appeal in Federated Learning.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Maximizing Global Model Appeal in Federated Learning

Reference 6

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This paper cites Exploiting shared representations for personalized federated learning.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Exploiting shared representations for personalized federated learning

Reference 7

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This paper cites Federated learning for predicting clinical outcomes in patients with covid-19.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Federated learning for predicting clinical outcomes in patients with covid-19

Reference 8

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This paper cites Statistical comparisons of classifiers over multiple data sets.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Statistical comparisons of classifiers over multiple data sets

Reference 9

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This paper cites A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian Regularization.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian Regularization

Reference 10

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This paper cites New Metrics to Evaluate the Performance and Fairness of Personalized Federated Learning.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning New Metrics to Evaluate the Performance and Fairness of Personalized Federated Learning

Reference 11

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This paper cites Learning to prune deep neural networks via layer-wise optimal brain surgeon.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Learning to prune deep neural networks via layer-wise optimal brain surgeon

Reference 12

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This paper cites Astraea: Self-balancing federated learning for improving classification accuracy of mobile deep learning applications.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Astraea: Self-balancing federated learning for improving classification accuracy of mobile deep learning applications

Reference 13

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This paper cites Layer-wise Model Pruning based on Mutual Information.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Layer-wise Model Pruning based on Mutual Information

Reference 14

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PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Learning both weights and connections for efficient neural network

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This paper cites Cross-Silo Federated Learning: Challenges and Opportunities.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Cross-Silo Federated Learning: Challenges and Opportunities

Reference 16

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This paper cites Fantastic Generalization Measures and Where to Find Them.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Fantastic Generalization Measures and Where to Find Them

Reference 17

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This paper cites Mimic-iii, a freely accessible critical care database.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Mimic-iii, a freely accessible critical care database

Reference 18

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This paper cites Advances and open problems in federated learning.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Advances and open problems in federated learning

Reference 19

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PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Similarity of neural network representations revisited

Reference 20

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PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Optimal brain damage

Reference 21

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PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Layer-wise adaptive model aggregation for scalable federated learning

Reference 22

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This paper cites Federated learning on non-iid data silos: An experimental study.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Federated learning on non-iid data silos: An experimental study

Reference 23

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PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Federated optimization in heterogeneous networks

Reference 24

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This paper cites Ditto: Fair and robust federated learning through personalization.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Ditto: Fair and robust federated learning through personalization

Reference 25

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PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Convergent Learning: Do different neural networks learn the same representations?

Reference 26

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This paper cites Learning efficient convolutional networks through network slimming.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Learning efficient convolutional networks through network slimming

Reference 27

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PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Rethinking the Value of Network Pruning

Reference 28

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This paper cites Layer-wised model aggregation for personalized federated learning.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Layer-wised model aggregation for personalized federated learning

Reference 29

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PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Understanding deep convolutional networks

Reference 30

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This paper cites Three Approaches for Personalization with Applications to Federated Learning.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Three Approaches for Personalization with Applications to Federated Learning

Reference 31

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PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Communication- efficient learning of deep networks from decentralized data

Reference 32

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PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Importance estimation for neural network pruning

Reference 33

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PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Insights on representational similarity in neural networks with canonical correlation

Reference 34

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This paper cites FedBABU: Towards Enhanced Representation for Federated Image Classification.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning FedBABU: Towards Enhanced Representation for Federated Image Classification

Reference 35

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PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning The Federated Tumor Segmentation (FeTS) Challenge

Reference 36

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PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Federated learning enables big data for rare cancer boundary detection

Reference 37

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PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Pruning algorithms-a survey

Reference 38

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PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 39

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PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Federated multi-task learning

Reference 40

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This paper cites Personalized federated learning with moreau envelopes.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Personalized federated learning with moreau envelopes

Reference 41

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PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings

Reference 42

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PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Towards personalized federated learning via heterogeneous model reassembly

Reference 43

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PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Towards understanding learning representations: To what extent do different neural networks learn the same representation

Reference 44

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PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Generalized shape metrics on neural representations

Reference 45

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This paper cites How transferable are features in deep neural networks? Advances in neural information processing systems, 27, 2014.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning How transferable are features in deep neural networks? Advances in neural information processing systems, 27, 2014

Reference 46

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PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Salvaging Federated Learning by Local Adaptation

Reference 47

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PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Visualizing and understanding convolutional networks

Reference 48

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PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Federated Learning with Non-IID Data

Reference 49

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PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Fedlp: Layer-wise pruning mechanism for communication-computation efficient federated learning

Reference 50

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