EF-Feddr: communication-efficient federated learning with Douglas–Rachford splitting and error feedback

Key Points

• Effective integration of error feedback enhances convergence in federated learning models.
• Utilizing relaxed Douglas-Rachford splitting improves the performance against compression issues.
• The approach addresses challenges related to communication in distributed learning systems.
• The findings may highlight the potential for more efficient data sharing in machine learning applications.