arXiv:2511.00269v1 Announce Type: new 
Abstract: Accurate classification plays a pivotal role in smart agriculture, enabling applications such as crop monitoring, fruit recognition, and pest detection. However, conventional centralized training often requires large-scale data collection, which raises privacy concerns, while standard federated learning struggles with non-independent and identically distributed (non-IID) data and incurs high communication costs. To address these challenges, we propose a federated learning framework that integrates a frozen Contrastive Language-Image Pre-training (CLIP) vision transformer (ViT) with a lightweight transformer classifier. By leveraging the strong feature extraction capability of the pre-trained CLIP ViT, the framework avoids training large-scale models from scratch and restricts federated updates to a compact classifier, thereby reducing transmission overhead significantly. Furthermore, to mitigate performance degradation caused by non-IID data distribution, a small subset (1%) of CLIP-extracted feature representations from all classes is shared across clients. These shared features are non-reversible to raw images, ensuring privacy preservation while aligning class representation across participants. Experimental results on agricultural classification tasks show that the proposed method achieve 86.6% accuracy, which is more than 4 times higher compared to baseline federated learning approaches. This demonstrates the effectiveness and efficiency of combining vision-language model features with federated learning for privacy-preserving and scalable agricultural intelligence.

FedReplay هو إطار مبتكر مصمم لتعزيز التعلم الفيدرالي الانتقالي في الزراعة الذكية، حيث يتناول التحديات الرئيسية مثل خصوصية البيانات وتكاليف الاتصال. من خلال تحسين دقة التصنيف لتطبيقات مثل مراقبة المحاصيل واكتشاف الآفات، لا يحمي هذا الإطار البيانات الحساسة فحسب، بل يعزز أيضًا الممارسات الزراعية الفعالة. هذا التطور مهم لأنه يمهد الطريق لتقنيات زراعية أكثر أمانًا وفعالية، مما يعود بالنفع في النهاية على المزارعين والمستهلكين على حد سواء.

FedReplay es un marco innovador diseñado para mejorar el aprendizaje por transferencia federado en la agricultura inteligente, abordando desafíos clave como la privacidad de los datos y los costos de comunicación. Al mejorar la precisión de la clasificación para aplicaciones como el monitoreo de cultivos y la detección de plagas, este marco no solo protege los datos sensibles, sino que también promueve prácticas agrícolas eficientes. Este desarrollo es significativo ya que allana el camino para tecnologías agrícolas más seguras y efectivas, beneficiando en última instancia a los agricultores y consumidores.

FedReplay est un cadre innovant conçu pour améliorer l'apprentissage par transfert fédéré dans l'agriculture intelligente, en répondant à des défis clés tels que la confidentialité des données et les coûts de communication. En améliorant la précision de classification pour des applications telles que la surveillance des cultures et la détection des ravageurs, ce cadre protège non seulement les données sensibles, mais favorise également des pratiques agricoles efficaces. Ce développement est important car il ouvre la voie à des technologies agricoles plus sécurisées et efficaces, bénéficiant finalement aux agriculteurs et aux consommateurs.

FedReplay is an innovative framework designed to enhance federated transfer learning in smart agriculture, addressing key challenges like data privacy and communication costs. By improving classification accuracy for applications such as crop monitoring and pest detection, this framework not only protects sensitive data but also promotes efficient farming practices. This development is significant as it paves the way for more secure and effective agricultural technologies, ultimately benefiting farmers and consumers alike.

FedReplay: A Feature Replay Assisted Federated Transfer Learning Framework for Efficient and Privacy-Preserving Smart Agriculture

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

FedReplay: A Feature Replay Assisted Federated Transfer Learning Framework for Efficient and Privacy-Preserving Smart Agriculture

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