Enhancing Federated Learning with Adaptive Client Selection and Resource Allocation

Federated learning (FL) offers a promising paradigm for privacy-preserving machine learning by enabling collaborative model training without centralizing raw data. This paper introduces an adaptive client selection and resource allocation strategy that significantly improves FL efficiency and performance. The proposed method dynamically chooses participating clients based on data quality and computational resources, and optimizes resource distribution to accelerate convergence and enhance model accuracy, making FL more practical for real-world decentralized applications.