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Making individualized treatment strategies and understanding how illnesses worsen in cancer research depend much on patient data. However, because this data is private, there are major privacy and security issues, particularly in cases of sharing across many companies. Since federated learning (FL), a decentralized machine learning tech 7th positionnique, enables models be trained on local datasets without transferring raw data, it might be a useful approach to undertake research that respects privacy. With an eye towards how Federated Learning may be used in cancer research, this article examines how it might enable individuals to collaborate while nevertheless adhering to rigorous privacy standards. Without compromising personal data, FL allows numerous hospitals and research facilities collaborate to create excellent prediction models for cancer diagnosis, prognosis, and optimal patient treatment. The paper also addresses technical issues like various kinds of data, model convergence, and system size that arise when attempting to use FL in cancer and offers solutions. It is also discussed how FL might be able to assist with moral and legal concerns with data sharing. This demonstrates how this approach could foster mutual trust while accelerating scientific advancement. Our aim with this work is to demonstrate that Federated Learning may be a practical and efficient approach to undertake cancer research safeguarding of privacy. It preserves the purity and value of medical data for enhancing cancer treatment even as it offers a scalable and safe alternative to conventional methods of data exchange.

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Federated Learning for Privacy-Preserving Oncology Research

  • 会议论文

  • 发表日期：

    2026年

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  • Dharmesh Dhabliya,
  • Vipan Kumar,
  • Nouby M. Ghazaly,
  • Bhavani Prasad Kasaraneni,
  • Sandeep Pushyamitra Pattyam,
  • Sateesh Kumar Nallamala,
  • Nischay Reddy Mitta,
  • Aditi Sharma

摘要

Making individualized treatment strategies and understanding how illnesses worsen in cancer research depend much on patient data. However, because this data is private, there are major privacy and security issues, particularly in cases of sharing across many companies. Since federated learning (FL), a decentralized machine learning tech 7th positionnique, enables models be trained on local datasets without transferring raw data, it might be a useful approach to undertake research that respects privacy. With an eye towards how Federated Learning may be used in cancer research, this article examines how it might enable individuals to collaborate while nevertheless adhering to rigorous privacy standards. Without compromising personal data, FL allows numerous hospitals and research facilities collaborate to create excellent prediction models for cancer diagnosis, prognosis, and optimal patient treatment. The paper also addresses technical issues like various kinds of data, model convergence, and system size that arise when attempting to use FL in cancer and offers solutions. It is also discussed how FL might be able to assist with moral and legal concerns with data sharing. This demonstrates how this approach could foster mutual trust while accelerating scientific advancement. Our aim with this work is to demonstrate that Federated Learning may be a practical and efficient approach to undertake cancer research safeguarding of privacy. It preserves the purity and value of medical data for enhancing cancer treatment even as it offers a scalable and safe alternative to conventional methods of data exchange.