arXiv Machine Learning September 29, 2026 By Tianyue Chu, Filippo Vannella, Dimitra Tsigkari, Paula Delgado-Santos, Fernando L\'opez, Pablo Gomez Guerrero, Sotirios Spantideas, David Solans Noguero

Distributed Learning as a Service: The Developer's Perspective

Read the original on arXiv Machine Learning →

The paper introduces Distributed Learning as a Service (DLaaS), a platform that lets developers launch distributed/federated learning jobs through a single admin dashboard. It offers declarative options such as Differential Privacy, Split Learning, Hierarchical Aggregation, and Knowledge Distillation without requiring changes to client code. The authors demonstrate the full service lifecycle on an industrial smart‑home Wake‑up Word task using the Ok Aura dataset, showing live operation across Android clients and Dockerized aggregators, and releasing the source code and video walkthroughs.

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