The paper introduces a novel Federated Edge Learning (FEEL) algorithm, named FEDMEGA, designed for low-earth-orbit (LEO) mega-constellation networks. FEDMEGA addresses the challenges of high mobility and short ground-to-satellite link (GSL) duration in LEO networks by integrating inter-satellite links (ISLs) for intra-orbit model aggregation. The algorithm reduces the usage of GSLs by increasing the frequency of intra-orbit training rounds and using a ring-all-reduced based intra-orbit aggregation mechanism. It also employs a network flow-based transmission scheme for global model aggregation, enhancing transmission efficiency. Theoretical convergence analysis is provided, and extensive simulations show that FEDMEGA outperforms existing satellite FEEL algorithms, achieving a 30% improvement in convergence rate. The algorithm's performance is evaluated on synthetic and real datasets, demonstrating better prediction accuracy and faster convergence.The paper introduces a novel Federated Edge Learning (FEEL) algorithm, named FEDMEGA, designed for low-earth-orbit (LEO) mega-constellation networks. FEDMEGA addresses the challenges of high mobility and short ground-to-satellite link (GSL) duration in LEO networks by integrating inter-satellite links (ISLs) for intra-orbit model aggregation. The algorithm reduces the usage of GSLs by increasing the frequency of intra-orbit training rounds and using a ring-all-reduced based intra-orbit aggregation mechanism. It also employs a network flow-based transmission scheme for global model aggregation, enhancing transmission efficiency. Theoretical convergence analysis is provided, and extensive simulations show that FEDMEGA outperforms existing satellite FEEL algorithms, achieving a 30% improvement in convergence rate. The algorithm's performance is evaluated on synthetic and real datasets, demonstrating better prediction accuracy and faster convergence.