Federated Learning for Colorectal Cancer Prediction

Yash Maurya, Prahaladh Chandrahasan, G. Poornalatha

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


The availability of datasets pertaining to various fields has increased significantly in the past decade, but there still exists a problem in getting datasets pertaining to the medical field as most of the data needs to be confidential and there exists laws which ensure a patient's data privacy. Federated learning (FL) proves to solve this problem via a client-server architecture by enabling distributed training of clients, without any data exposure. In this paper, we apply the FedAvg (FederatedAveraging) [1] algorithm on the PathMNISTv2 [2] dataset for predicting colorectal cancer. We also present a refined convolutional neural network (CNN) architecture for accurate predictions on the PathMNISTv2 dataset. We have studied the effects on IID (Independent and Identically Distributed) and Non-IID (Non-Identically Independently Distributed) distributions in a distributed environment. We have also compared these results with a centralized model and demonstrate that FedAvg achieves similar results in a distributed setting. We anticipate our study to enable additional healthcare studies driven by vast and diverse data, and illustrate the efficacy of FL at such magnitude and task complexity as a paradigm shift for multi-site partnerships, eliminating the need for data sharing.

Original languageEnglish
Title of host publication2022 IEEE 3rd Global Conference for Advancement in Technology, GCAT 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665468534
Publication statusPublished - 2022
Event3rd IEEE Global Conference for Advancement in Technology, GCAT 2022 - Bangalore, India
Duration: 07-10-202209-10-2022

Publication series

Name2022 IEEE 3rd Global Conference for Advancement in Technology, GCAT 2022


Conference3rd IEEE Global Conference for Advancement in Technology, GCAT 2022

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Information Systems
  • Information Systems and Management
  • Media Technology
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications


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