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This chapter explores the future of cross-device federated recommendation systems, focusing on benchmarks, heterogeneous federated learning, knowledge graphs, and encryption techniques. It highlights the need for standardized protocols for fair evaluations and addresses model adaptability, communication efficiency, and equity in heterogeneous settings. The chapter discusses knowledge graphs for improved node representations and advanced encryption for enhanced data security. It provides insights and outlines research directions to ensure scalability and applicability in real-world scenarios.

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Future Prospects

  • 章节

  • 发表日期：

    2025年

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  • Xiangjie Kong,
  • Lingyun Wang,
  • Mengmeng Wang,
  • Guojiang Shen

摘要

This chapter explores the future of cross-device federated recommendation systems, focusing on benchmarks, heterogeneous federated learning, knowledge graphs, and encryption techniques. It highlights the need for standardized protocols for fair evaluations and addresses model adaptability, communication efficiency, and equity in heterogeneous settings. The chapter discusses knowledge graphs for improved node representations and advanced encryption for enhanced data security. It provides insights and outlines research directions to ensure scalability and applicability in real-world scenarios.