  Federated Learning is a distributed machine learning framework designed for
data privacy preservation i.e., local data remain private throughout the entire
training and testing procedure. Federated Learning is gaining popularity
because it allows one to use machine learning techniques while preserving
privacy. However, it inherits the vulnerabilities and susceptibilities raised
in deep learning techniques. For instance, Federated Learning is particularly
vulnerable to data poisoning attacks that may deteriorate its performance and
integrity due to its distributed nature and inaccessibility to the raw data. In
addition, it is extremely difficult to correctly identify malicious clients due
to the non-Independently and/or Identically Distributed (non-IID) data. The
real-world data can be complex and diverse, making them hardly distinguishable
from the malicious data without direct access to the raw data. Prior research
has focused on detecting malicious clients while treating only the clients
having IID data as benign. In this study, we propose a method that detects and
classifies anomalous clients from benign clients when benign ones have non-IID
data. Our proposed method leverages feature dimension reduction, dynamic
clustering, and cosine similarity-based clipping. The experimental results
validates that our proposed method not only classifies the malicious clients
but also alleviates their negative influences from the entire procedure. Our
findings may be used in future studies to effectively eliminate anomalous
clients when building a model with diverse data.
