Robust federated learning is an emerging paradigm in machine learning that addresses the challenges of training accurate and secure models in decentralized and privacy-constrained environments. By leveraging the power of collaborative learning, this paradigm also ensures robustness against model attackers. However, federated learning setups are especially vulnerable against various targeted attacks including label-flipping and backdoor attacks. To combat this, similarity between client weight updates has gained increased traction as a reliable metric for attacker detection. In this chapter, we describe some of the works tackling targeted attacks by leveraging model similarity. We then present a graph theoretic formulation that leverages model correlations and introduce two novel graph theoretic algorithms MST-AD and Density-AD for the detection of targeted adversaries. The limitations of similarity based algorithms in distributed attack settings are then acknowledged. To combat these attacks, we introduce a divergence-based algorithm called Div-DBAD and establish its superiority on distributed backdoor attacks done on the setup. Experimental analysis on two standard machine learning datasets establishes the superiority of the Density-AD and the MST-AD algorithms against targeted attacks and the Div-DBAD algorithm against distributed backdoor attacks. For both the scenarios, the proposed algorithms are able to outperform the existing state of the art and maintain a lower success rate for the attacks while observing minimal drops in model performance.