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cs.LG2026
Distributed Learning as a Service: The Developer's Perspective
Tianyue Chu, Filippo Vannella, Dimitra Tsigkari +5
Application developers of distributed learning services face challenges that a typical federated learning loop does not address. Specifically, the model updates can still leak priv…
cs.LG2025
Data Heterogeneity and Forgotten Labels in Split Federated Learning
Joana Tirana, Dimitra Tsigkari, David Solans Noguero +1
In Split Federated Learning (SFL), the clients collaboratively train a model with the help of a server by splitting the model into two parts. Part-1 is trained locally at each clie…