Federated learning has become increasingly popular due to its ability to bring together multiple learners, enhance model generalizability, and promote knowledge exchange. Such systems inherently rely on the bedrock of security, trust, and fairness among training workers to ensure a conducive learning environment. However, this collaborative landscape has encountered the challenge of free riders, individuals who join the systems to gain benefits without making any substantial contributions. This can negatively impact learning outcomes, fairness, and sustainability of a collaborative system. In this paper, we first present a novel stake-based incentive mechanism to maximize the reward for clients, thereby encouraging active participation from contributors. Second, we propose an efficient method for identifying free riders in federated learning based on submission log analysis. Our method delegates the detection of free riders to training workers and the identification to the aggregator, rather than relying solely on the aggregator. We explore potential deceptive strategies employed by free riders and assess the extent of our method's coverage across these scenarios. Additionally, experimental results conducted on different free rider ratios also demonstrate the versatility and applicability of our approach in detecting these clients within the federated learning paradigm.