arXiv:2412.18833v2 Announce Type: replace 
Abstract: In medical imaging, developing generalized segmentation models that can handle multiple organs and lesions is crucial. However, the scarcity of fully annotated datasets and strict privacy regulations present significant barriers to data sharing. Federated Learning (FL) allows decentralized model training, but existing FL methods often struggle with partial labeling, leading to model divergence and catastrophic forgetting. We propose ConDistFL, a novel FL framework incorporating conditional distillation to address these challenges. ConDistFL enables effective learning from partially labeled datasets, significantly improving segmentation accuracy across distributed and non-uniform datasets. In addition to its superior segmentation performance, ConDistFL maintains computational and communication efficiency, ensuring its scalability for real-world applications. Furthermore, ConDistFL demonstrates remarkable generalizability, significantly outperforming existing FL methods in out-of-federation tests, even adapting to unseen contrast phases (e.g., non-contrast CT images) in our experiments. Extensive evaluations on 3D CT and 2D chest X-ray datasets show that ConDistFL is an efficient, adaptable solution for collaborative medical image segmentation in privacy-constrained settings.

تُحدث طريقة جديدة تُدعى ConDistFL ضجة في مجال التصوير الطبي من خلال معالجة التحديات المتعلقة بتطوير نماذج تقسيم البيانات مع بيانات محدودة. تستخدم هذه الطريقة التعلم الفيدرالي لتمكين تدريب النماذج بشكل لامركزي مع احترام لوائح الخصوصية. من خلال معالجة مشكلات مثل تباين النموذج والنسيان الكارثي، تعد ConDistFL بتحسين دقة وموثوقية تقنيات التصوير الطبي، وهو أمر حاسم لتحقيق نتائج أفضل للمرضى.

Un nuevo enfoque llamado ConDistFL está causando sensación en el campo de la imagen médica al abordar los desafíos de desarrollar modelos de segmentación con datos etiquetados limitados. Este método aprovecha el aprendizaje federado para permitir el entrenamiento de modelos descentralizados mientras respeta las regulaciones de privacidad. Al abordar problemas como la divergencia del modelo y el olvido catastrófico, ConDistFL promete mejorar la precisión y la fiabilidad de las tecnologías de imagen médica, lo cual es crucial para mejores resultados en los pacientes.

Une nouvelle approche appelée ConDistFL fait sensation dans le domaine de l'imagerie médicale en s'attaquant aux défis liés au développement de modèles de segmentation avec des données étiquetées limitées. Cette méthode utilise l'apprentissage fédéré pour permettre un entraînement de modèle décentralisé tout en respectant les réglementations sur la vie privée. En s'attaquant à des problèmes tels que la divergence des modèles et l'oubli catastrophique, ConDistFL promet d'améliorer la précision et la fiabilité des technologies d'imagerie médicale, ce qui est crucial pour de meilleurs résultats pour les patients.

A new approach called ConDistFL is making waves in the field of medical imaging by addressing the challenges of developing segmentation models with limited labeled data. This method leverages federated learning to enable decentralized model training while respecting privacy regulations. By tackling issues like model divergence and catastrophic forgetting, ConDistFL promises to enhance the accuracy and reliability of medical imaging technologies, which is crucial for better patient outcomes.

Federated Learning with Partially Labeled Data: A Conditional Distillation Approach

arXiv:2601.08614v1 Announce Type: cross 
Abstract: Heterogeneity within data distribution poses a challenge in many modern federated learning tasks. We formalize it as an optimization problem involving a computationally heavy composite under data similarity. By employing different sets of assumptions, we present several approaches to develop communication-efficient methods. An optimal algorithm is proposed for the convex case. The constructed theory is validated through a series of experiments across various problems.

أظهرت دراسة حديثة تحديات التباين في توزيع البيانات ضمن مهام التعلم الفيدرالي كمسألة تحسين، حيث اقترحت عدة طرق فعالة في التواصل وخوارزمية مثالية للحالة المحدبة. وقد تم التحقق من النظرية من خلال سلسلة من التجارب عبر مشاكل متنوعة.

Un estudio reciente ha formalizado los desafíos que plantea la heterogeneidad en la distribución de datos dentro de las tareas de aprendizaje federado como un problema de optimización, proponiendo varios métodos de comunicación eficientes y un algoritmo óptimo para el caso convexo. La teoría ha sido validada a través de experimentos en varios problemas.

Une étude récente a formalisé les défis posés par l'hétérogénéité dans la distribution des données au sein des tâches d'apprentissage fédéré en tant que problème d'optimisation, proposant plusieurs méthodes de communication efficaces et un algorithme optimal pour le cas convexe. La théorie a été validée par des expériences sur divers problèmes.

A recent study has formalized the challenges posed by heterogeneity in data distribution within federated learning tasks as an optimization problem, proposing several communication-efficient methods and an optimal algorithm for the convex case. The theory has been validated through experiments across various problems.

Accelerated Methods with Complexity Separation Under Data Similarity for Federated Learning Problems

One More Thing in AI – Your Shortcut to AI Mastery

Federated Learning with Partially Labeled Data: A Conditional Distillation Approach

Was this article worth reading? Share it

One More Thing in AI

LucidQuery AI

Octofy

Attentive AI

Https

Superset

Ready to build your own newsroom?