3 citations · 5 across the 5 of their papers we have counts for
Showing cs.IRShow all
2 papers · 1 filter
cs.IR2023
FedFNN: Faster Training Convergence Through Update Predictions in Federated Recommender Systems
Francesco Fabbri, Xianghang Liu, Jack R. McKenzie +2
Federated Learning (FL) has emerged as a key approach for distributed machine learning, enhancing online personalization while ensuring user data privacy. Instead of sending privat…
cs.IR2022★ 2 cited
Online Meta-Learning for Model Update Aggregation in Federated Learning for Click-Through Rate Prediction
Xianghang Liu, Bartłomiej Twardowski, Tri Kurniawan Wijaya
In Federated Learning (FL) of click-through rate (CTR) prediction, users' data is not shared for privacy protection. The learning is performed by training locally on client devices…