BEGIN:VCALENDAR VERSION:2.0 PRODID:icalendar-ruby CALSCALE:GREGORIAN BEGIN:VEVENT DTSTAMP:20260407T181343Z UID:70f612a2-7482-498f-a8ac-07917c552a2b DTSTART:20260427T090000Z DTEND:20260427T170000Z 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 (Visual Stud io Code\, Python\, flower pip package) and installation instructions.\n\n\ n## Schedule \nThe course 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 ready to use algorithm \n* Theory block 2: FL for Bio informatics \n* Practical block 2: Group exercise developing a basic FL al gorithm \n\n## Application\nThe registration fees for academics are **100 CHF** and **500 CHF** for for-profit companies.\n\nYou will be informed by email of your registration confirmation. Upon reception of the confirmati on email\, participants will be asked to confirm attendance by paying the fees within 5 days.\n\nApplications close on **06/04/2026** or as soon as the places will be filled out. Deadline for free-of-charge cancellation is set to **13/04/2026**. Cancellation after this date will not be reimburse d. Please note that participation in SIB courses is subject to our [genera l conditions](https://www.sib.swiss/training/terms-and-conditions).\n\n## Venue and Time\nThis course will take place in East Campus USI-SUPSI\, Lug ano-Viganello.\n\nThe course will start at 9:00 and end around 17:00. \n\n Precise information will be provided to the participants in due time.\n\n\ n## Additional information\nCoordination: Patricia Palagi\, SIB Training G roup\n\nWe will recommend 0.25 ECTS credits for this course (given a passe d exam at the end of the course).\n\nYou are welcome to register to the SI B courses mailing list to be informed of all future courses and workshops\ , as well as all important deadlines using the form [here](https://lists.s ib.swiss/postorius/lists/courses.lists.sib.swiss/).\n\nPlease note that pa rticipation in SIB courses is subject to our [general conditions](https:// www.sib.swiss/training/terms-and-conditions).\n\nSIB abides by the [ELIXIR Code of Conduct](https://elixir-europe.org/events/code-of-conduct). Parti cipants of SIB courses are also required to abide by the same code.\n\nFor more information\, please contact [training@sib.swiss](mailto://training@ sib.swiss). SUMMARY:Federated Learning in Bioinformatics URL;VALUE=URI:https://www.sib.swiss/training/course/20260427_FEDBX END:VEVENT END:VCALENDAR