I. Introduction
The way federated learning has a strong partnership with CNN shows us how overall development in agricultural innovation can be obtained while different kinds of plant diseases are being tackled, especially in jute farming. Jute, also an emblem of wealth and valuable for its economic contribution, is on the verge of extinction due to numerous diseases on leave [1]. Not only do these diseases deny plant well-being, but they also lead to losses of economic nature throughout the world, particularly in India, given the latter's position in world agriculture. Here, we bring out a methodology based on federated learning techniques for treating jute leaf diseases, which can be classified under five major categories using CNN models. This strategy could improve crop-raising efficiency and support the locals who rely on jute cultivation [2]. This research identifies and categorizes five central diseases affecting jute leaves: blight, Alternaria leaf spot, Cercospora leaf spot, powdery mildew, and stem rot. Every illness and disease exhibit different symptoms, which must be recognized and treated correctly. This research aims to leverage the powers of federated learning to refute the teaching simultaneously across five distinct clients representing five different illness groups. With such measures in place, data utilisation can be decoupled from data ownership, preventing nonconsensual use and safeguarding data security at the same time [3]. This analytical breakthrough helps identify diseases and allows the agricultural sector to access modern technology, a significant challenge in different areas because of economic imbalances. Not only does it deal with the technical aspects, but the major socio-economic issues confronting the jutegrowing community, especially in India, are also issues that this study explores. The paper is about an occasion where agricultural disease control will be scalable and sustainable and create a federated learning framework that studies and learns quickly from distance information. Instead of being relied on just for situations with a large-scale establishment or when the resources are plentiful, this new method empowers even small-scale farming enterprises to use AI technology to reduce the economic effects of jute leaf diseases [4]. Convolutional neural networks (CNNs) in federated learning help improve the classification of diseases by allowing the decision-making process for disease management to be done in a timely and effective manner [5]. For the first time, this work employs the novel approach of federated learning in the context of agriculture. It offers a model that can help devise collaborative and privacypreserving ways of combating disease-causing plants that are hastily spread. The discovery of their bacteriophages certainly has far-reaching implications for controlling such diseases. This can effectively build resilience in juteproducing countries, enhance agricultural sustainability, and fight global problems linked to food poverty [6]. Our goal is that this study marks an era when federated learning and CNNs are integral parts of disease control and innovative farming methods, and its high resolution helps the interests of all agricultural stakeholders worldwide. leafThis research work makes joint use of the federated learning methods with the help of CNNs, which led to solving the issue of jute leaf diseases. The technical solution provides a novel approach to crop management. It emphasises the capability of AI in coworking to reform disease management strategies for better plant health, to uphold economic interests, and to meet the challenges of the sustainable development of agriculture. Also, it emphasises how revelatory the power of AI in coworking is in India and around the world [7].