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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.
Comparing centralised machine learning and federated learning using flower framework. Building a custom strategy over the base FedAvg called FedCustom which has a higher learning rate and several other hyper parameters to increase the accuracy.