Learning in federated and dynamic environments: A tutorial on challenges, trends, and practical strategies

Federated learning enables privacy-preserving machine learning across distributed data sources, but real-world deployments face challenges that extend beyond standard protocols. This tutorial provides a structured overview of the field, addressing issues such as non-stationary data, client heterogeneity, resource constraints, and security threats. Beyond existing surveys, it incorporates hands-on insights and deployment experiences, including concrete war stories illustrating how federated learning methods perform under real-world conditions. The tutorial also outlines emerging directions, including federated graph learning, game-theoretic approaches, and sustainable AI concepts. It aims to provide both a conceptual framework and practical guidance for researchers and practitioners advancing federated learning in dynamic and evolving environments. • A unified tutorial on federated learning and non-stationary environments combining practical strategies and state-of-the-art insights. • Includes taxonomy of challenges and hands-on techniques such as uncertainty-aware sampling, resource-constrained adaptation, and cooperative incentive mechanisms. • Provides use cases and war stories addressing challenges in machine learning for federated learning and non-stationary environments.