  Objective: Federated Learning (FL) enables collaborative model training while
keeping data locally. Currently, most FL studies in radiology are conducted in
simulated environments due to numerous hurdles impeding its translation into
practice. The few existing real-world FL initiatives rarely communicate
specific measures taken to overcome these hurdles, leaving behind a significant
knowledge gap. Minding efforts to implement real-world FL, there is a notable
lack of comprehensive assessment comparing FL to less complex alternatives.
Materials & Methods: We extensively reviewed FL literature, categorizing
insights along with our findings according to their nature and phase while
establishing a FL initiative, summarized to a comprehensive guide. We developed
our own FL infrastructure within the German Radiological Cooperative Network
(RACOON) and demonstrated its functionality by training FL models on lung
pathology segmentation tasks across six university hospitals. We extensively
evaluated FL against less complex alternatives in three distinct evaluation
scenarios. Results: The proposed guide outlines essential steps, identified
hurdles, and proposed solutions for establishing successful FL initiatives
conducting real-world experiments. Our experimental results show that FL
outperforms less complex alternatives in all evaluation scenarios, justifying
the effort required to translate FL into real-world applications. Discussion &
Conclusion: Our proposed guide aims to aid future FL researchers in
circumventing pitfalls and accelerating translation of FL into radiological
applications. Our results underscore the value of efforts needed to translate
FL into real-world applications by demonstrating advantageous performance over
alternatives, and emphasize the importance of strategic organization, robust
management of distributed data and infrastructure in real-world settings.
