  Federated Learning (FL) is a collaborative learning paradigm enabling
participants to collectively train a shared machine learning model while
preserving the privacy of their sensitive data. Nevertheless, the inherent
decentralized and data-opaque characteristics of FL render its susceptibility
to data poisoning attacks. These attacks introduce malformed or malicious
inputs during local model training, subsequently influencing the global model
and resulting in erroneous predictions. Current FL defense strategies against
data poisoning attacks either involve a trade-off between accuracy and
robustness or necessitate the presence of a uniformly distributed root dataset
at the server. To overcome these limitations, we present FedZZ, which harnesses
a zone-based deviating update (ZBDU) mechanism to effectively counter data
poisoning attacks in FL. Further, we introduce a precision-guided methodology
that actively characterizes these client clusters (zones), which in turn aids
in recognizing and discarding malicious updates at the server. Our evaluation
of FedZZ across two widely recognized datasets: CIFAR10 and EMNIST, demonstrate
its efficacy in mitigating data poisoning attacks, surpassing the performance
of prevailing state-of-the-art methodologies in both single and multi-client
attack scenarios and varying attack volumes. Notably, FedZZ also functions as a
robust client selection strategy, even in highly non-IID and attack-free
scenarios. Moreover, in the face of escalating poisoning rates, the model
accuracy attained by FedZZ displays superior resilience compared to existing
techniques. For instance, when confronted with a 50% presence of malicious
clients, FedZZ sustains an accuracy of 67.43%, while the accuracy of the
second-best solution, FL-Defender, diminishes to 43.36%.
