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%.

FedZZ通过使用基于区域的偏离更新（ZBDU）机制来有效对抗联邦学习中的数据污染攻击，同时还引入了一种精确引导的方法来主动对客户端集群进行特征化，从而帮助识别和丢弃服务器上的恶意更新。在对CIFAR10和EMNIST这两个广泛认可的数据集进行的评估中，FedZZ证明了在单个和多个客户端攻击情景以及不同攻击强度下，其在抵御数据污染攻击方面的有效性超过了现有技术方法的表现。此外，在面对日益增长的污染率时，相比现有技术，FedZZ达到的模型准确性显示出更强的韧性。例如，当存在50%的恶意客户端时，FedZZ的准确性保持在67.43%，而第二好的解决方案FL-Defender的准确性降至43.36%。

精确引导方法应对联邦学习中的数据投毒攻击