The semiconductor industry faces complex problems such as root-cause analysis and predicting maintainance which can benefit from collaboration between multiple parties. However, strict confidentiality requirements prevent such cooperation. We enable collaborative machine learning while protecting proprietary information.

Publications

Collaborative and Confidential Junction Trees for Hybrid Bayesian Networks

Roberto Gheda, Abele Malan, Thiago Guzella, Carlo Lancia, Robert Birke, Lydia Y. Chen

NeurIPS 2025: Paper|Code

Citation 
@inproceedings{ghedacollaborative,
  title={Collaborative and Confidential Junction Trees for Hybrid Bayesian Networks},
  author={Gheda, Roberto and M{\u{a}}lan, Abele and Guzella, Thiago and Lancia, Carlo and Birke, Robert and Chen, Lydia Y},
  booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems}
}

Share Secrets for Privacy: Confidential Forecasting with Vertical Federated Learning**

Aditya Shankar, Jeremie Decouchant, Dimitra Gkorou, Rihan Hai, Lydia Y. Chen

ARES 2025 Paper|Code

Citation 
@inproceedings{DBLP:conf/IEEEares/ShankarDGHC25,
  author       = {Aditya Shankar and
                  J{\'{e}}r{\'{e}}mie Decouchant and
                  Dimitra Gkorou and
                  Rihan Hai and
                  Lydia Y. Chen},
  editor       = {Mila Dalla Preda and
                  Sebastian Schrittwieser and
                  Vincent Naessens and
                  Bjorn De Sutter},
  title        = {Share Secrets for Privacy: Confidential Forecasting with Vertical
                  Federated Learning},
  booktitle    = {Availability, Reliability and Security - 20th International Conference,
                  {ARES} 2025, Ghent, Belgium, August 11-14, 2025, Proceedings, Part
                  {I}},
  series       = {Lecture Notes in Computer Science},
  volume       = {15992},
  pages        = {358--379},
  publisher    = {Springer},
  year         = {2025},
  url          = {https://doi.org/10.1007/978-3-032-00624-0\_18},
  doi          = {10.1007/978-3-032-00624-0\_18},
  timestamp    = {Sat, 06 Sep 2025 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/conf/IEEEares/ShankarDGHC25.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

CCBNet: Confidential Collaborative Bayesian Networks Inference**

Abele Malan, Jeremie Decouchant, Thiago Guzella, Lydia Y. Chen

FC 2025 Paper|Code

Citation 
@article{DBLP:journals/corr/abs-2405-15055,
  author       = {Abele Malan and
                  J{\'{e}}r{\'{e}}mie Decouchant and
                  Thiago Guzella and
                  Lydia Y. Chen},
  title        = {CCBNet: Confidential Collaborative Bayesian Networks Inference},
  journal      = {CoRR},
  volume       = {abs/2405.15055},
  year         = {2024},
  url          = {https://doi.org/10.48550/arXiv.2405.15055},
  doi          = {10.48550/ARXIV.2405.15055},
  eprinttype    = {arXiv},
  eprint       = {2405.15055},
  timestamp    = {Wed, 19 Jun 2024 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2405-15055.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}