Federated learning enables distributed clients to train a global model while maintaining privacy and control over their data. Although collaboration can substantially improve the learning process, it also introduces vulnerabilities, as not all participants contribute beneficially. Clients may engage in detrimental activities, such as data poisoning, that compromise the integrity of the global model. Additionally, in a realistic scenario, the quality of the data possessed by these clients is highly heterogeneous, which influences the model training performance. Moreover, malicious clients (aka free riders) intend to obtain the global model without making a real contribution to the training process. Hence, a reliable and fair evaluation of the client contribution is essential to promote diverse client engagement, improve robustness, and address the free-rider problem. This paper proposes a reputation-aware contribution evaluation approach (RBFL) that provides adversarial robustness by tracking reputation over multiple training rounds to ensure that clients consistently contribute positively. We employ CosineGradient as the utility function and Truncated Monte Carlo (TMC) Shapley as the data valuation function. Empirical evaluation demonstrates the effectiveness of our approach in a fair evaluation of clients’ contributions and effective identification of adversarial clients while maintaining a model accuracy of $92 \%$ with adversarial robustness.