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journal article Jul 29, 2025

Communication-Efficient Federated Neural Collaborative Filtering with Multi-Armed Bandits

WAQAR ALI ORCID Muhammad Ammad-Ud-Din ORCID Xiangmin Zhou ORCID Jie Shao ORCID
ACM Transactions on Recommender Systems Vol. 4 No. 1 pp. 1-28 · Association for Computing Machinery (ACM)
View at Publisher Save 10.1145/3651168
Abstract
Federated learning (FL) has received much attention in privacy-preserving and responsible recommender systems. Recent studies have shown promising results while federating widely used recommendation methods such as collaborative filtering. A major barrier when bringing FL into production is that the model complexity or the volume of gradients to be transmitted over the communication channel grows linearly as the number of items in a particular system increases. To address this challenge, we propose a communication-efficient neural collaborative filtering method for federated recommender systems. First, to align our solution with other deep neural architectures, we construct standard neural collaborative filtering in federated settings. Second, to solve the underlying model complexity challenge, a multi-armed bandit framework is used that intelligently selects a smaller set of payloads for each iteration of federated model training. The item selection is based on a carefully designed reward function that determines which portion of the overall payloads would be optimal for a particular user. The FL model only comprising of the selected items is transmitted over the network. The FL users train their local models in the regular federated learning way utilizing the payload-efficient global model, requiring no additional optimizations. The results show that using only 10% of the model’s payload, our method can achieve recommendation performance comparable with the standard federated neural collaborative filtering.
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Showing 50 of 57 references

Metrics
11
Citations
57
References
Details
Published
Jul 29, 2025
Vol/Issue
4(1)
Pages
1-28
Authors
W
WAQAR ALI

Sichuan Artificial Intelligence Research Institute; Yibin Park, University of Electronic Science and Technology of China

M
Muhammad Ammad-Ud-Din

Comparables.ai Oy

X
Xiangmin Zhou

RMIT University

J
Jie Shao

University of Electronic Science and Technology of China; Sichuan Artificial Intelligence Research Institute

Funding
National Natural Science Foundation of China Award: 62276047
Sichuan Science and Technology Program Award: 2022YFWZ0006
Australian Research Council Discovery project Award: DP200101175
Cite This Article
WAQAR ALI, Muhammad Ammad-Ud-Din, Xiangmin Zhou, et al. (2025). Communication-Efficient Federated Neural Collaborative Filtering with Multi-Armed Bandits. ACM Transactions on Recommender Systems, 4(1), 1-28. https://doi.org/10.1145/3651168
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