Efficient entropy-guided byzantine attacker identification in federated learning

Abstract

One example method includes performing various operations in a federated learning environment that includes nodes configured and operable to communicate with a central server. The operations include gathering aggregate gradient information from clusters that each include a respective group of some of the nodes, examining the respective gradient information for each of the clusters, and identifying outlier clusters based on the respective aggregate gradient information of those clusters, for each of the clusters identified as an outlier cluster, setting all the nodes in those clusters as suspect nodes, and performing a clustering operation that comprises reassigning each of the suspect nodes to one of the clusters, and a decision to reassign any given one of the suspect nodes is based on an amount of information expected to be obtained as a result of that decision.