Decentralized Knowledge Acquisition for Mobile Internet Applications

Jing Jiang, Shaoxiong Ji, Guodong Long*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)


Mobile internet applications on smart phones dominate large portions of daily life for many people. Conventional machine learning-based knowledge acquisition methods collect users’ data in a centralized server, then train an intelligent model, such as recommendation and prediction, using all the collected data. This knowledge acquisition method raises serious privacy concerns, and also violates the rules of the newly published General Data Protection Regulation. This paper proposes a new attention-augmented federated learning framework that can conduct decentralized knowledge acquisition for mobile Internet application scenarios, such as mobile keyboard suggestions. In particular, the attention mechanism aggregates the decentralized knowledge which has been acquired from each mobile using its own data locally. The centralized server aggregates knowledge without direct access to personal data. Experiments on three real-world datasets demonstrate that the proposed framework performs better than other baseline methods in terms of perplexity and communication cost.

Original languageEnglish
Pages (from-to)2653-2669
Number of pages17
JournalWorld Wide Web
Issue number5
Early online date13 Mar 2020
Publication statusPublished - Sep 2020
MoE publication typeA1 Journal article-refereed


  • Decentralized knowledge acquisition
  • Federated learning
  • Mobile internet applications


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