Preventing Text Data Poisoning Attacks in Federated Machine Learning by an Encrypted Verification Key

Recent studies show significant security problems with most of the Federated Learning models. There is a false assumption that the participant is not the attacker and would not use poisoned data. This vulnerability allows attackers to use polluted data to train their data locally and send the model updates to the edge server for aggregation, which generates an opportunity for data poisoning. In such a setting, it is challenging for an edge …