FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios | Paper Citation Record

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FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios Federated Learning Based on Dynamic Regularization

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FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios Unresolved cited work

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FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios Unresolved cited work

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FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios Towards Federated Long-Tailed Learning

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FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios Momentum Benefits Non-IID Federated Learning Simply and Provably

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FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios Unresolved cited work

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FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios Unresolved cited work

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FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios Unresolved cited work

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FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios Unresolved cited work

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This paper cites Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization.

FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization

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FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios Federated Learning via Synthetic Data

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FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios Interference-free Walks in Time: Temporally Disjoint Paths

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FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios Unresolved cited work

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FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data

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FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

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FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios Neural Collapse: A Review on Modelling Principles and Generalization

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FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios Krizhevsky and G

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FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios

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FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregation

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FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios Adaptive Federated Optimization

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FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Features

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FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios On the product formula for Toeplitz and related operators

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FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios CLIP-guided Federated Learning on Heterogeneous and Long-Tailed Data

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FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios Unresolved cited work

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FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum

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FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

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FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Data

Reference 51

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FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios Unresolved cited work

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FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios FedCM: Federated Learning with Client-level Momentum

Reference 53

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FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios Unresolved cited work

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FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios Unresolved cited work

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FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios Unresolved cited work

Reference 56

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FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios A Survey on Class Imbalance in Federated Learning

Reference 57

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FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios Unresolved cited work

Reference 58

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FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios Unresolved cited work

Reference 59

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FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios Shear flow in a three-dimensional complex plasma in microgravity conditions

Reference 60

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FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios Unresolved cited work

Reference 61

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FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios In2020 USENIX Annual Technical Conference (USENIX ATC 20)

Reference 2020

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Pith citing papers