arXiv Machine Learning By Mohamad Mestoukirdi, Vincent Corlay

Expected Gain-based Escalation in Vertical Federated Learning

Read the original on arXiv Machine Learning →

arXiv:2606. 31331v1 Announce Type: new Abstract: Collaborative inference can improve predictive performance by integrating complementary information across agents, but applying collaborative fusion to every sample can incur unnecessary communication and computational overhead.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Jul 21

Online-Score-Aided Federated Learning for Resource-Constrained Wireless Clients with Continual Data Arrival

arXiv:2408. 05886v5 Announce Type: replace Abstract: Heterogeneous system configurations of distributed clients connected to the central server (CS) via a time-varying wireless network pose significant challenges for popular distributed machine learning (ML) algorithms such as federated learning (FL).

By Ferdous Pervej, Minseok Choi, Andreas F. Molisch