  Federated learning (FL) enables multiple clients to collaboratively train
deep learning models while considering sensitive local datasets' privacy.
However, adversaries can manipulate datasets and upload models by injecting
triggers for federated backdoor attacks (FBA). Existing defense strategies
against FBA consider specific and limited attacker models, and a sufficient
amount of noise to be injected only mitigates rather than eliminates FBA. To
address these deficiencies, we introduce a Flexible Federated Backdoor Defense
Framework (Fedward) to ensure the elimination of adversarial backdoors. We
decompose FBA into various attacks, and design amplified magnitude
sparsification (AmGrad) and adaptive OPTICS clustering (AutoOPTICS) to address
each attack. Meanwhile, Fedward uses the adaptive clipping method by regarding
the number of samples in the benign group as constraints on the boundary. This
ensures that Fedward can maintain the performance for the Non-IID scenario. We
conduct experimental evaluations over three benchmark datasets and thoroughly
compare them to state-of-the-art studies. The results demonstrate the promising
defense performance from Fedward, moderately improved by 33% $\sim$ 75 in
clustering defense methods, and 96.98%, 90.74%, and 89.8% for Non-IID to the
utmost extent for the average FBA success rate over MNIST, FMNIST, and CIFAR10,
respectively.
