Federated learning: From design foundations to advanced participant selection paradigms

Abstract

Federated Learning (FL) has emerged as a promising paradigm owing to its significant advantages over conventional centralized machine learning approaches. It is a decentralized learning framework in which a global model is iteratively constructed by aggregating locally trained models from distributed clients, thereby ensuring enhanced data privacy while reducing communication overhead and latency. This survey provides a comprehensive yet concise overview of the state-of-the-art in FL covering key architectural aspects, i.e., scale of federation, data distribution, network topology, exchanged information, training procedures, machine learning models, and aggregation techniques. It further reviews widely used FL frameworks and datasets for simulation and evaluation, incorporating an analysis of dataset selection strategies, the limitations of synthetic data partitioning, and the gap between simulated and real-world deployments. In addition, the incorporation of FL in various applications for their rapid acceleration is discussed. The challenges pertinent to FL are examined under two main categories, i.e., general challenges and those specific to the participant selection process. Building on this, the survey positions participant selection as a central design component in FL systems and provides a unified analysis of state-of-the-art methods through the lenses of composability, trade-offs, and practical design considerations, highlighting its critical role in achieving efficient, robust, fair, and scalable FL.