<ul><li>Fairness in federated learning is a rapidly growing research area focusing on formal definitions and algorithmic interventions but often overlooks sociotechnical contexts.</li><li>Existing approaches in fairness for federated learning optimize system level metrics and ignore harms throughout the FL lifecycle and their impact on diverse stakeholders.</li><li>Critical analysis of the literature exposes recurring issues like framing fairness only through server-client architecture, simulation-context mismatches, and lack of multi-stakeholder alignment.</li><li>A harm-centered framework is proposed to connect fairness definitions to risks and stakeholder vulnerabilities, advocating for more thorough and accountable fairness research in federated learning.</li></ul>

Fairness in Federated Learning: Fairness for Whom?

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