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Related Experiment Videos

Bias-Corrected Federated Learning for Video Recommendation over Stochastic Communication Links.

Chaochen Zhou1,2, Yadong Pei3, Zhidu Li1

  • 1School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.

Entropy (Basel, Switzerland)
|May 4, 2026
PubMed
Summary

This study introduces a bias-corrected federated learning framework for video recommendations, improving accuracy and robustness in real-time services over unreliable networks.

Keywords:
bias correctionfederated learningstatistical aggregationvideo recommendation

Related Experiment Videos

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computer Science

Background:

  • Growing demand for privacy-preserving, real-time personalized services on large-scale video platforms.
  • Need for robust federated recommendation frameworks over practical communication networks.

Purpose of the Study:

  • Propose a bias-corrected federated learning framework for video recommendation over stochastic communication links.
  • Address timeliness requirements and communication unreliability in federated learning.

Main Methods:

  • Introduced a bias-corrected mechanism at the local training stage to mitigate feature-level bias by accounting for video duration and user activity.
  • Analytically characterized the successful upload probability of local model transmission under time-varying channel conditions.
  • Designed a statistically corrected global aggregation strategy to maintain unbiased global updates despite potential node failures.

Main Results:

  • The proposed framework significantly improves recommendation accuracy.
  • Demonstrated robustness against communication unreliability in practical distributed environments.
  • Learned representations more accurately reflect users' intrinsic preferences.

Conclusions:

  • The bias-corrected federated learning framework effectively enhances video recommendation systems.
  • The framework ensures reliable and accurate personalized services even with communication constraints.
  • Validated through comprehensive experimental evaluations in distributed settings.