Real-World Federated Learning in Radiology: Hurdles to overcome and Benefits to gain

• URL: http://arxiv.org/abs/2405.09409v1
• Date: Wed, 15 May 2024 15:04:27 GMT
• Title: Real-World Federated Learning in Radiology: Hurdles to overcome and Benefits to gain
• Authors: Markus R. Bujotzek, Ünal Akünal, Stefan Denner, Peter Neher, Maximilian Zenk, Eric Frodl, Astha Jaiswal, Moon Kim, Nicolai R. Krekiehn, Manuel Nickel, Richard Ruppel, Marcus Both, Felix Döllinger, Marcel Opitz, Thorsten Persigehl, Jens Kleesiek, Tobias Penzkofer, Klaus Maier-Hein, Rickmer Braren, Andreas Bucher,
• Abstract summary: Federated Learning (FL) enables collaborative model training while keeping data locally. Currently, most FL studies in radiology are conducted in simulated environments. Few existing real-world FL initiatives rarely communicate specific measures taken to overcome these hurdles.
• Score: 2.8048919658768523
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• A Survey on Efficient Federated Learning Methods for Foundation Model Training [66.19763977571114]
  Federated Learning (FL) has become an established technique to facilitate privacy-preserving collaborative training across a multitude of clients. In the wake of Foundation Models (FM), the reality is different for many deep learning applications. We discuss the benefits and drawbacks of parameter-efficient fine-tuning (PEFT) for FL applications.
  arXiv  Detail & Related papers  (2024-01-09T10:22:23Z)
• FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participants [41.070716405671206]
  Federated learning (FL) provides a privacy-preserving approach for collaborative training of machine learning models. It is crucial to select appropriate collaborators for each FL participant based on data complementarity. It is imperative to consider the inter-individual relationships among FL-PTs where some FL-PTs engage in competition.
  arXiv  Detail & Related papers  (2023-12-18T17:53:01Z)
• Which mode is better for federated learning? Centralized or Decentralized [64.46017397813549]
  Both centralized and decentralized approaches have shown excellent performance and great application value in federated learning (FL) However, current studies do not provide evidence to show which one performs better.
  arXiv  Detail & Related papers  (2023-10-05T11:09:42Z)
• A Comprehensive View of Personalized Federated Learning on Heterogeneous Clinical Datasets [0.4926316920996346]
  Federated learning (FL) is a key approach to overcoming the data silos that so frequently obstruct the training and deployment of machine-learning models in clinical settings. This work contributes to a growing body of FL research specifically focused on clinical applications along three important directions.
  arXiv  Detail & Related papers  (2023-09-28T20:12:17Z)
• Understanding How Consistency Works in Federated Learning via Stage-wise Relaxed Initialization [84.42306265220274]
  Federated learning (FL) is a distributed paradigm that coordinates massive local clients to collaboratively train a global model. Previous works have implicitly studied that FL suffers from the client-drift'' problem, which is caused by the inconsistent optimum across local clients. To alleviate the negative impact of the client drift'' and explore its substance in FL, we first design an efficient FL algorithm textitFedInit.
  arXiv  Detail & Related papers  (2023-06-09T06:55:15Z)
• FS-Real: Towards Real-World Cross-Device Federated Learning [60.91678132132229]
  Federated Learning (FL) aims to train high-quality models in collaboration with distributed clients while not uploading their local data. There is still a considerable gap between the flourishing FL research and real-world scenarios, mainly caused by the characteristics of heterogeneous devices and its scales. We propose an efficient and scalable prototyping system for real-world cross-device FL, FS-Real.
  arXiv  Detail & Related papers  (2023-03-23T15:37:17Z)
• When Federated Learning Meets Pre-trained Language Models' Parameter-Efficient Tuning Methods [22.16636947999123]
  We introduce various parameter-efficient tuning (PETuning) methods into federated learning. Specifically, we provide a holistic empirical study of representative PLMs tuning methods in FL. Overall communication overhead can be significantly reduced by locally tuning and globally aggregating lightweight model parameters.
  arXiv  Detail & Related papers  (2022-12-20T06:44:32Z)
• ISFL: Federated Learning for Non-i.i.d. Data with Local Importance Sampling [17.29669920752378]
  We propose importance sampling federated learning (ISFL), an explicit framework with theoretical guarantees. We derive the convergence theorem of ISFL to involve the effects of local importance sampling. We employ a water-filling method to calculate the IS weights and develop the ISFL algorithms.
  arXiv  Detail & Related papers  (2022-10-05T09:43:58Z)
• UniFed: All-In-One Federated Learning Platform to Unify Open-Source Frameworks [53.20176108643942]
  We present UniFed, the first unified platform for standardizing open-source Federated Learning (FL) frameworks. UniFed streamlines the end-to-end workflow for distributed experimentation and deployment, encompassing 11 popular open-source FL frameworks. We evaluate and compare 11 popular FL frameworks from the perspectives of functionality, privacy protection, and performance.
  arXiv  Detail & Related papers  (2022-07-21T05:03:04Z)
• FederatedScope: A Comprehensive and Flexible Federated Learning Platform via Message Passing [63.87056362712879]
  We propose a novel and comprehensive federated learning platform, named FederatedScope, which is based on a message-oriented framework. Compared to the procedural framework, the proposed message-oriented framework is more flexible to express heterogeneous message exchange. We conduct a series of experiments on the provided easy-to-use and comprehensive FL benchmarks to validate the correctness and efficiency of FederatedScope.
  arXiv  Detail & Related papers  (2022-04-11T11:24:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.