Mobile Information Systems

Personalized Distributed Machine Learning for Mobile Services

Publishing date
01 Jan 2023
Submission deadline
26 Aug 2022

Lead Editor

1Kennesaw State University, Marietta, USA

2North Carolina A&T, Greensboro, USA

3University of Electronic Science and Technology of China, Chengdu, China

Personalized Distributed Machine Learning for Mobile Services


The advancement of machine learning techniques has been recognized as a primary enabler of novel mobile services and applications. Among these novel technical solutions, distributed machine learning has led to the emerging phenomenon of mobile networks, which is being used across diverse industry verticals such as intelligent economy, smart transportation, and digital supply chain for better intelligence and performance.

However, the distributed machine learning paradigms, when connected with various services and applications, could have different demands in the quality of service, training target, and budget constraints. For example, smart city services may always expect a real-time model while a smart health care application cares more about how to improve the model’s accuracy. Furthermore, in novel distributed paradigms such as federated learning, the demand heterogeneity could be more complex because of the asymmetric information challenge. Despite the existing advantages of the machine learning empowered systems for various mobile services and industrial applications, a highly personalized distributed machine learning solution is expected to offer customized functions to meet the individual requirements in performance, privacy, and efficiency, and with reasonable computation and communication overheads.

This Special Issue aims to present state-of-the-art and contributions towards the various trends and challenges in personalization problems in distributed machine learning for mobile networks. Original research and review articles are welcome.

Potential topics include but are not limited to the following:

  • Novel theories and paradigms of the convergence of personal mobile services
  • Personalized AI-based big data and analysis for mobile services
  • Knowledge graph and related middleware for semantic analysis of mobile data on-demand
  • Platforms or testbeds for customized mobile services
  • Trustworthy model design based on individual requirements for mobile services
  • Privacy-aware personal data sharing for mobile services
  • Privacy-aware individual incentive schemes for mobile services
  • QoS-aware resource allocation solution for personalized machine learning mobile service

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