Abnormal Local Clustering in Federated Learning

Federated Learning has been issued as an extraordinary model for preserving private data. It makes the machine learning model in the global model train without sharing personal and private data which is distributed to many other local devices. However, it is also unable to access the data and analyze it to tell which local data is poisoned[1]. With this disadvantage, Federated learning is easily exposed to Sybil attacks such as transfers of faulty results to the global model. FoolsGold[2], those key is that Sybil's training directions are more similar to other Sybil's than typical average local's directions, is known as one of the best algorithms for defense from Sybil attacks.