Federated Learning (FL) is a machine learning approach that enables multiple clients to collaboratively train a model while keeping their data decentralized rather than centrally stored. A key characteristic of FL is data heterogeneity—since client data remains decentralized, the data samples across clients are often non-independent and non-identically distributed. FL has numerous applications, particularly in self-driving cars, healthcare, medical AI, robotics, and biometrics. However, one of its major challenges is ensuring robustness against malicious clients. During the learning process, some clients may be compromised by adversarial attacks, leading to data corruption and model manipulation. This project aims to develop robust FL techniques that can detect and mitigate malicious clients during training, ensuring they are identified and excluded from the learning process to maintain model integrity.