Overview

In recent years, there has been increasing concern about the risks associated with data usage, particularly regarding data sharing. This has led to the introduction of regulations which impose stricter rules on data management. These regulations significantly impact scientific research, especially in the biomedical field, where the sensitivity of the data makes multi-center studies more challenging to conduct.

To address these challenges, federated learning (FL) has gained popularity. FL allows multiple parties to collaboratively train a shared machine learning model using their individual data sources without sharing the data itself, thereby enhancing privacy and security. This is typically realized with the help of a server that receives non-sensitive information from data-holder parties (e.g., parameters from a locally trained model) and aggregates it into a global model.

This course will give an overview of FL concepts, including the operational framework, privacy benefits, and challenges. It will show how FL can be used in bioinformatics, covering both federated versions of established bioinformatics algorithms and federated machine learning algorithms designed for bioinformatics data. In hands-on group exercises, a FL consortium will be simulated using the open-source FL platform FeatureCloud and a basic FL algorithm will be developed.

Audience

This course is addressed to life scientists and bioinformaticians, from academia or industry, with an interest in machine learning for bioinformatics applications.

Learning outcomes

At the end of the course, the participants are expected to:

Develop an understanding of FL concepts, including its operational framework, privacy benefits, and challenges.
Gain an overview of federated methods in bioinformatics, including federated equivalents of established bioinformatics algorithms as well as federated machine learning algorithms applied to bioinformatics data.
Acquire hands-on experience using federated learning platforms, such as FeatureCloud.
Understand the process of developing a federated learning algorithm through hands-on experience in a didactic exercise.

Prerequisites

Knowledge / competencies

Participants should have a basic knowledge of statistics, machine learning, and Python. No previous knowledge on FL is required.

The competences and knowledge levels required correspond to those taught in courses such as: First Steps with Python in Life Sciences, Introduction to Machine Learning with Python, and Introduction to statistics with R. Test your skills with Python and statistics with the quiz here, before registering.

Technical

You are required to bring your own laptop. Before the course begins, participants will receive a guide detailing the necessary software for the practical activities (Python, Docker, featurecloud pip package) and installation instructions.

Schedule

The course is organized into 4 sessions:

Theory block 1: Introduction to FL
Practical block 1: Group exercise simulating a FL consortium with a ready to use algorithm
Theory block 2: FL for Bioinformatics
Practical block 2: Group exercise developing a basic FL algorithm

Application

The registration fees for academics are 100 CHF and 500 CHF for for-profit companies.

You will be informed by email of your registration confirmation. Upon reception of the confirmation email, participants will be asked to confirm attendance by paying the fees within 5 days.

Applications close on 22/08/2025 or as soon as the places will be filled out. Deadline for free-of-charge cancellation is set to 29/08/2025. Cancellation after this date will not be reimbursed. Please note that participation in SIB courses is subject to our general conditions.

Venue and Time

This course will take place in East Campus USI-SUPSI, Lugano-Viganello.

The course will start at 9:00 and end around 17:00.

Precise information will be provided to the participants in due time.

Additional information

Coordination: Patricia Palagi, SIB Training Group

We will recommend 0.25 ECTS credits for this course (given a passed exam at the end of the course).

You are welcome to register to the SIB courses mailing list to be informed of all future courses and workshops, as well as all important deadlines using the form here.

Please note that participation in SIB courses is subject to our general conditions.

SIB abides by the ELIXIR Code of Conduct. Participants of SIB courses are also required to abide by the same code.

For more information, please contact training@sib.swiss.

Federated Learning in Bioinformatics