fedntd

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Lee-Gihun / FedNTD

(NeurIPS 2022) Official Implementation of "Preservation of the Global Knowledge by Not-True Distillation in Federated Learning"

machine-learning deep-neural-networks computer-vision deep-learning scaffold artificial-intelligence moon knowledge-distillation federated-learning continual-learning distributed-learning fedavg fedprox fedavgm fedcurv feddyn fedntd fednova

Updated Feb 24, 2023
Python

Lee-Gihun / FedSOL

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(CVPR 2024) Official Implementation of "FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning"

machine-learning computer-vision deep-learning optimization scaffold moon federated-learning continual-learning distributed-learning federated-learning-framework fedavg fedprox sharpness-aware-minimization fedntd fednova fedsol fedsam perfedavg knnper

Updated Jun 28, 2024
Python

gautamHCSCV / Federated-Learning-Methods-Comparison

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We utilize the Adversarial Model Perturbations (AMP) regularizer to regularize clients’ models. The AMP regulzaizer is based on perturbing the model parameters so as to get a more generalized model. The claim of AMP regularizer is to reach flat minima and therefore is expected to reach flat minima in FL settings as well.

scaffold pytorch federated-learning fedavg fedprox fedntd amp-regularizer

Updated May 24, 2023
Python

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