Online Meta-Learning for Model Update Aggregation in Federated Learning for Click-Through Rate Prediction. (arXiv:2209.00629v1 [cs.IR])

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 and communicating only model changes to the
server. There are two main challenges: (i) the client heterogeneity, making FL
algorithms that use the weighted averaging to aggregate model updates from the
clients have slow progress and unsatisfactory learning results; and (ii) the
difficulty of tuning the server learning rate with trial-and-error methodology
due …

aggregation arxiv click federated learning learning meta meta-learning prediction rate