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DTSTAMP:20260709T074630Z
UID:f4617d5e-5cf0-43f5-a6a8-2e848b7ddcdf
DTSTART:20250829T090000Z
DTEND:20250829T170000Z
DESCRIPTION:## Overview\nIn recent years\, there has been increasing concer
 n about the risks associated with data usage\, particularly regarding data
  sharing. This has led to the introduction of regulations which impose str
 icter rules on data management. These regulations significantly impact sci
 entific research\, especially in the biomedical field\, where the sensitiv
 ity of the data makes multi-center studies more challenging to conduct. \n
 \nTo address these challenges\, federated learning (FL) has gained popular
 ity. FL allows multiple parties to collaboratively train a shared machine 
 learning model using their individual data sources without sharing the dat
 a itself\, thereby enhancing privacy and security. This is typically reali
 zed 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.  \n\nThis 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\, coveri
 ng both federated versions of established bioinformatics algorithms and fe
 derated machine learning algorithms designed for bioinformatics data. In h
 ands-on group exercises\, a FL consortium will be simulated using the open
 -source FL platform framework Flower ([https://flower.ai/](https://flower.
 ai/))  and a basic FL algorithm will be developed. \n\n## Audience\nThis c
 ourse is addressed to life scientists and bioinformaticians\, from acade
 mia or industry\, with an interest in machine learning for bioinformatics 
 applications. \n\n## Learning outcomes\nAt the end of the course\, the par
 ticipants are expected to:\n\n- Develop an understanding of FL concepts\, 
 including its operational framework\, privacy benefits\, and challenges.\n
 \n- Gain an overview of federated methods in bioinformatics\, including fe
 derated equivalents of established bioinformatics algorithms as well as fe
 derated machine learning algorithms applied to bioinformatics data.\n\n- A
 cquire hands-on experience using a federated learning framework (Flower)
 .\n\n- Understand the process of developing a federated learning algorithm
  through hands-on experience in a didactic exercise.\n\n## Prerequisites\n
 ##### Knowledge / competencies\nParticipants should have a basic knowledge
  of statistics\, machine learning\, and Python. No previous knowledge on F
 L is required. \n \n\nThe competences and knowledge levels required corres
 pond to those taught in courses such as: [First Steps with Python in Life 
 Sciences](https://www.sib.swiss/training/course/20240304_FSWP)\, [Introduc
 tion to Machine Learning with Python](https://www.sib.swiss/training/cours
 e/20240527_INMLP)\, and [Introduction to statistics with R](https://www.si
 b.swiss/training/website/course/20230206_STATR). Test your skills with Pyt
 hon and statistics with the quiz [here](https://forms.gle/ZpQFyHHwoPQKJSwv
 7)\, before registering. \n##### Technical\nYou 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\, fl
 ower pip package) and installation instructions. \n\n## Schedule \nThe cou
 rse is organized into 4 sessions: \n\n* Theory block 1: Introduction to FL
  \n* Practical block 1: Group exercise simulating a FL consortium with a r
 eady to use algorithm \n* Theory block 2: FL for Bioinformatics \n* Practi
 cal block 2: Group exercise developing a basic FL algorithm \n\n## Applica
 tion\nThe registration fees for academics are **100 CHF** and **500 CHF** 
 for for-profit companies.\n\nYou will be informed by email of your registr
 ation confirmation. Upon reception of the confirmation email\, participant
 s will be asked to confirm attendance by paying the fees within 5 days.\n\
 nApplications close on **08/08/2025** or as soon as the places will be fil
 led out. Deadline for free-of-charge cancellation is set to **15/08/2025**
 . Cancellation after this date will not be reimbursed. Please note that pa
 rticipation in SIB courses is subject to our [general conditions](https://
 www.sib.swiss/training/terms-and-conditions).\n\n## Venue and Time\nThis c
 ourse will take place in East Campus USI-SUPSI\, Lugano-Viganello.\n\nThe 
 course will start at 9:00 and end around 17:00. \n\nPrecise information wi
 ll be provided to the participants in due time.\n\n\n## Additional informa
 tion\nCoordination: Patricia Palagi\, SIB Training Group\n\nWe will recomm
 end 0.25 ECTS credits for this course (given a passed exam at the end of t
 he course).\n\nYou are welcome to register to the SIB courses mailing list
  to be informed of all future courses and workshops\, as well as all impor
 tant deadlines using the form [here](https://lists.sib.swiss/postorius/lis
 ts/courses.lists.sib.swiss/).\n\nPlease note that participation in SIB cou
 rses is subject to our [general conditions](https://www.sib.swiss/training
 /terms-and-conditions).\n\nSIB abides by the [ELIXIR Code of Conduct](http
 s://elixir-europe.org/events/code-of-conduct). Participants of SIB courses
  are also required to abide by the same code.\n\nFor more information\, pl
 ease contact [training@sib.swiss](mailto://training@sib.swiss).
SUMMARY:Federated Learning in Bioinformatics
URL;VALUE=URI:https://www.sib.swiss/training/course/20250829_FEDBX
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