Bilkent University
Department of Computer Engineering

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CS 590/690 SEMINAR

 

Privacy-Preserving CNV Calling through Homomorphically Encrypted Federated Fine-Tuning of ECOLE

 

Ahmet Arda Ceylan
Master Student
(Supervisor:Asst.Prof.Ercüment Çiçek)

Computer Engineering Department
Bilkent University

Abstract: Accurate detection of Copy Number Variants (CNVs) in Whole Exome Sequencing (WES) data is critical for diagnosing genetic disorders and cancer. While the ECOLE model establishes a new state-of-the-art by utilizing a transformer-based architecture to achieve high precision and recall, its clinical utility often depends on fine-tuning with small, expert curated datasets or specific tumor samples. However, the centralized collection of such highly sensitive genomic data poses significant privacy and regulatory hurdles. This presentation introduces a novel framework for privacy-preserving transfer learning on the ECOLE model. We propose a Federated Learning (FL) architecture that enables multi institutional collaboration without the exchange of raw patient data. To ensure cryptographic security during the aggregation of model updates, we utilize Homomorphic Encryption (HE). Our approach strategically freezes the core transformer blocks and performs HE enabled fine-tuning exclusively on the remaining layers.

 

DATE: March 09, Monday @ 15:30 Place: EA 502