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

Year: 2026

Type: article

Abstract: 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.

Source: Neurocomputing

DOI: 10.1016/j.neucom.2026.132671

Authors: Mirko Polato, Barbara Hammer, Manuel Röder, Frank-Michael Schleif

Institutions: University of Turin, Bielefeld University, Technical University of Applied Sciences Würzburg-Schweinfurt

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,670

Funders: European Commission, Ministerium für Kultur und Wissenschaft des Landes Nordrhein-Westfalen, European Regional Development Fund, International Council of Shopping Centers