Fed-Reputed: Reputation-Aware Client Selection in Hierarchical Federated Learning for Consumer Electronics

Federated Learning (FL) enables collaborative model training across distributed consumer devices without requiring raw data to be centralized - an essential feature for privacy-critical applications such as biomedical monitoring and mobile health diagnostics. However, real-world deployments often face challenges in client selection, especially in the presence of misbehaved clients. Straggler clients with poor connectivity or low resources delay convergence, while malicious clients pose threats to model integrity by injecting poisoned updates. These problems are amplified in dynamic, heterogeneous environments such as those involving consumer biomedical devices. To address this, we propose Fed-Reputed, a reputation-aware client selection framework tailored for Hierarchical Federated Learning (HFL). Unlike existing reputation-based approaches that suffer from herding effects, cold-start limitations, and imbalanced classification datasets, Fed-Reputed integrates a modified Bellman equation within a Deep Q-Learning framework. This formulation guides client selection using an Imbalanced Classification Markov Decision Process (ICMDP), leveraging both device capability and historical behavior. Extensive simulations using MNIST and FMNIST datasets under varying percentages of straggler and malicious clients demonstrate that Fed-Reputed achieves up to 50% higher global model accuracy and 1.7 times faster convergence compared to state-of-the-art selection methods. Moreover, it significantly improves the detection of misbehaving clients without sacrificing fairness or scalability. These results highlight the potential of Fed-Reputed to advance secure, scalable, and personalized Federated Learning in health-sensitive consumer environments.