Deep learning empowers enormous scientific advances, with key applications in healthcare. It has been widely accepted that it is possible to achieve better models with growing amounts of data. However, enabling learning on these huge datasets or training huge models in a timely manner requires to distribute the learning on several devices. One particularity in the medical domain, and in the medical imaging setting is that data sharing across different institutions often becomes impractical due to strict privacy regulations, making the collection of large-scale centralized datasets practically impossible.

Some of the problems, therefore, become: how can we train models in a distributed way on several devices? And is it possible to achieve models as strong as those that can be trained on large centralized datasets without sharing data and breaching the restrictions on privacy and property? Distributed machine learning, including Federated Learning (FL) approaches, could be helpful to solve the latter problem. Different institutions can contribute to building more powerful models by performing collaborative training without sharing any training data. The trained model can be distributed across various institutions but not the actual data. We hope that with FL and other forms of distributed and collaborative learning, the objective of training better, more robust models of higher clinical utility while still protecting the privacy within the data can be achieved.

Weight Erosion: an Update Aggregation Scheme for Personalized Collaborative Machine Learning, Felix H M Grimberg (EPFL); Mary-Anne Hartley (EPFL); Martin Jaggi (EPFL); Sai Praneeth Karimireddy (EPFL)

Fed-BioMed: A general open-source frontend framework for federated learning in healthcare, Santiago Silva (INRIA); Andre Altmann (Stanford University); Boris Gutman (Illinois Institute of Technology); Marco Lorenzi (INRIA)

On the Fairness of Privacy-Preserving Representations in Medical Applications, Mhd Hasan Sarhan (Technical University of Munich); Nassir Navab (Technical University of Munich); Abouzar Eslami (Carl Zeiss Meditec AG.); Shadi Albarqouni (ETH Zurich)

Siloed Federated Learning for Multi-Centric Histopathology Datasets, Jean Ogier du Terrail (Owkin, Inc.); Mathieu Andreux (Owkin, Inc.); Constance Beguier (Owkin, Inc.); Eric W Tramel (Owkin, Inc.)

Inverse Distance Aggregation for Federated Learning with Non-IID Data, Yousef Yeganeh (Technical University of Munich); Azade Farshad (Technical University of Munich); Nassir Navab (Technical University of Munich); Shadi Albarqouni (ETH Zurich)

Federated Learning for Breast Density Classification: A Real-World Implementation, Holger R Roth (NVIDIA); Ken Chang (Harvard); Praveer Singh (Harvard); Wenqi Li (NVIDIA); Nir Neumark (); Vikash Gupta (OSU); Min Yun (Harvard); Evan Leibovitz (Partners); Bernardo Bizzo (MGH-BWH CCDS); Daguang Xu (NVIDIA Corporation); Jayashree Kalpathy-Cramer (MGH/Harvard Medical School); Alvin Ihsani (NVIDIA ); Yuhong Wen (NVIDIA); Varun Buch (MGH-BWH CCDS); Adam McCarthy (MGH-BWH CCDS); Katharina Hoebel (MIT); Jay Patel (MIT); Ittai Dayan (Partners); Ram Naidu (MGH & BWH Center for Clinical Data Science); Thomas Schultz (Partners); Prerna Dogra (NVIDIA); Andy Feng (NVIDIA); Yan Cheng (NVIDIA); Selnur Erdal (OSU); Richard White (OSU); Keith Dreyer (Partners); Felipe C Kitamura (Diagnósticos da América (DASA)); Meesam Shah (ACR); Laura Coombs (ACR); Ahmed Harouni (NVIDIA); Jeffrey Hawley (OSU); Miao Zhang (Stanford); Liangqiong Qu (Stanford University); Jesse Tetreault (Nvidia); Sharut Gupta (QTIM); Mona Flores (NVIDIA); Daniel Rubin (Stanford University); Jayashree Kalpathy-Cramer (Massachusetts General Hospital)

Federated Gradient Averaging for Multi-Site Training with Momentum-Based Optimizers, Samuel W Remedios (Johns Hopkins University); John Butman (National Institutes of Health); Bennett A Landman (Vanderbilt University); Dzung Pham (National Institutes of Health)

Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning, Chen Shen (Nagoya University); Pochuan Wang (National Taiwan University); Holger R Roth (NVIDIA); Dong Yang (NVIDIA Corporation); Daguang Xu (NVIDIA Corporation); Masahiro Oda (Nagoya University); Kazunari Misawa (Aichi Cancer Center); Po-Ting Chen (National Taiwan University Hospital); Wei-Chih Liao (National Taiwan University Hospital); Kao-Lang Liu (National Taiwan University Hospital); Weichung Wang (National Taiwan University); Kensaku Mori (Nagoya University)