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Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game

  • Decentralized learning allows training of deep learning algorithms without centralizing datasets, improving data privacy and operational efficiency.
  • Data imbalances in distributed learning, especially in medical fields, pose challenges due to different patient populations and data collection practices.
  • The paper proposes two algorithms, DSWM and ASWM, for setting weights of each node's contribution in the global model.
  • The ASWM algorithm significantly improves the performance of underrepresented nodes by 2.713% in AUC, while nodes with larger datasets experience only a modest decrease of 0.441%.

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