Cluster Based Secure Multi-party Computation in Federated Learning for Histopathology Images

Seyedeh Maryam Hosseini, Milad Sikaroudi, Morteza Babaei, Hamid R. Tizhoosh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Federated learning (FL) is a decentralized method enabling hospitals to collaboratively learn a model without sharing private patient data for training. In FL, participant hospitals periodically exchange training results rather than training samples with a central server. However, having access to model parameters or gradients can expose private training data samples. To address this challenge, we adopt secure multiparty computation (SMC) to establish a privacy-preserving federated learning framework. In our proposed method, the hospitals are divided into clusters. After local training, each hospital splits its model weights among other hospitals in the same cluster such that no single hospital can retrieve other hospitals’ weights on its own. Then, all hospitals sum up the received weights, sending the results to the central server. Finally, the central server aggregates the results, retrieving the average of models’ weights and updating the model without having access to individual hospitals’ weights. We conduct experiments on a publicly available repository, The Cancer Genome Atlas (TCGA). We compare the performance of the proposed framework with differential privacy and federated averaging as the baseline. The results reveal that compared to differential privacy, our framework can achieve higher accuracy with no privacy leakage risk at a cost of higher communication overhead.

Original languageEnglish (US)
Title of host publicationDistributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health - 3rd MICCAI Workshop, DeCaF 2022, and 2nd MICCAI Workshop, FAIR 2022, Held in Conjunction with MICCAI 2022, Proceedings
EditorsShadi Albarqouni, Spyridon Bakas, Sophia Bano, M. Jorge Cardoso, Bishesh Khanal, Bennett Landman, Xiaoxiao Li, Chen Qin, Islem Rekik, Nicola Rieke, Holger Roth, Daguang Xu, Debdoot Sheet
PublisherSpringer Science and Business Media Deutschland GmbH
Pages110-118
Number of pages9
ISBN (Print)9783031185229
DOIs
StatePublished - 2022
Event3rd MICCAI Workshop on Distributed, Collaborative, and Federated Learning, DeCaF 2022, and the 2nd MICCAI Workshop on Affordable AI and Healthcare, FAIR 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022 - Singapore, Singapore
Duration: Sep 22 2022Sep 22 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13573 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd MICCAI Workshop on Distributed, Collaborative, and Federated Learning, DeCaF 2022, and the 2nd MICCAI Workshop on Affordable AI and Healthcare, FAIR 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022
Country/TerritorySingapore
CitySingapore
Period9/22/229/22/22

Keywords

  • Decentralized learning
  • Federated learning
  • Histopathology imaging
  • Privacy preservation
  • Secure multiparty computation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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