The author explores the severity of data poisoning attacks in computer networks, highlighting the ineffectiveness of label flipping attacks and the success of feature poisoning attacks in fooling servers.
The author explores the impact of poisoning attacks in Decentralized Federated Learning, focusing on the methodology to assess these attacks and their effects. By extending a gossipy simulator with an attack injector module, the study evaluates poisoning attacks in gossip learning algorithms.