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Online Meta-Learning for Model Update Aggregation in Federated Learning for Click-Through Rate Prediction


Xianghang Liu, Bartłomiej Twardowski and 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 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 to the big computation time and resources needed
for each experiment. To address these challenges, we propose a
simple online meta-learning method to learn a strategy of aggregat-
ing the model updates, which adaptively weighs the importance of
the clients based on their attributes and adjust the step sizes of the
update. We perform extensive evaluations on public datasets. Our
method significantly outperforms the state-of-the-art in both the
speed of convergence and the quality of the final learning results

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