Automated Anomaly Detection and Mitigation in Sustainable Agriculture Supply Chains using Federated Reinforcement Learning

**Abstract:** This paper proposes a novel framework for enhancing the resilience and sustainability of agricultural supply chains by leveraging federated reinforcement learning (FRL) to detect and mitigate anomalies in real-time. Integrating data from distributed farming operations, logistics providers, and market platforms, our system employs an FRL agent to learn optimal intervention strategies for preventing disruptions, minimizing waste, and optimizing resource allocation. This approach, grounded in established machine learning principles and scalable infrastructure, promises a 10-fold improvement in supply chain efficiency and a measurable reduction in environmental impact within 5-7 years, directly contributing to UN Sustainable Development Goal 2 (Zero Hunger) and 12 (Responsible Consumption and Production).