3 papers
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.CR2024
FedQV: Leveraging Quadratic Voting in Federated Learning
Tianyue Chu, Nikolaos Laoutaris
Federated Learning (FL) permits different parties to collaboratively train a global model without disclosing their respective local labels. A crucial step of FL, that of aggregatin…
cs.LG2023
PriPrune: Quantifying and Preserving Privacy in Pruned Federated Learning
Tianyue Chu, Mengwei Yang, Nikolaos Laoutaris +1
Federated learning (FL) is a paradigm that allows several client devices and a server to collaboratively train a global model, by exchanging only model updates, without the devices…