Designed a Byzantine-robust decentralized machine learning algorithm which uses a binary classifier as a filter. Derived theoretical convergence, stability and generalization bounds. Tested the algorithm through simulations on various 20-node networks (small-world topologies, random geometric graphs, Erdos-Renyi graphs). Results verified that total Byzantine edge weight and the classifier's FNR directly impact the out-of-sample test accuracy.