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Research

I am actively involved in research at the intersection of distributed learning, federated learning, and competitive environments. My work focuses on developing mechanisms to optimize collaboration and competition in machine learning systems, particularly in cross-silo federated learning and SplitFed learning settings.

Publications #

  • Dachille, J., Huang, C., and Liu, X. (2025). The Impact of Cut Layer Selection in Split Federated Learning. AAAI FLUID Workshop ‘25. [link] [paper]
  • Huang, C., Dachille, J., and Liu, X. (2024). Coopetition in Heterogeneous Cross-Silo Federated Learning. ECAI ‘24. [link] [paper]
  • Huang, C., Dachille, J., and Liu, X. (2024). An Accuracy-Shaping Mechanism for Competitive Distributed Learning. ICANN ‘24. [link] [paper]
  • Huang, C., Dachille, J., and Liu, X. (2024). When Federated Learning Meets Oligopoly Competition: Stability and Model Differentiation. IEEE Internet of Things Journal. [link] [paper]