  This paper proposes and investigates a new approach for detecting and
preventing several different types of poisoning attacks from affecting a
centralized Federated Learning model via average accuracy deviation detection
(AADD). By comparing each client's accuracy to all clients' average accuracy,
AADD detect clients with an accuracy deviation. The implementation is further
able to blacklist clients that are considered poisoned, securing the global
model from being affected by the poisoned nodes. The proposed implementation
shows promising results in detecting poisoned clients and preventing the global
model's accuracy from deteriorating.
