Federated learning has become a significant approach for training machine learning models using decentralized data without necessitating the sharing of this data. Recently, the incorporation of generative artificial intelligence (AI) methods has provided new possibilities for improving privacy, augmenting data, and customizing models. This research explores potential integrations of generative AI in federated learning, revealing various opportunities to enhance privacy, data efficiency, and model performance. It particularly emphasizes the importance of generative models like generative adversarial networks (GANs) and variational autoencoders (VAEs) in creating synthetic data that replicates the distribution of real data. Generating synthetic data helps federated learning address challenges related to limited data availability and supports robust model development. Additionally, we examine various applications of generative AI in federated learning that enable more personalized solutions.

翻译：联邦学习已成为一种利用去中心化数据训练机器学习模型的重要方法，且无需共享原始数据。近年来，生成式人工智能方法的引入为提升隐私保护、增强数据多样性和实现模型定制提供了新的可能性。本研究探讨了生成式人工智能在联邦学习中的潜在融合方式，揭示了其在增强隐私性、数据效率和模型性能方面的多种机遇。研究特别强调了生成对抗网络和变分自编码器等生成模型在创建合成数据以模拟真实数据分布方面的重要性。生成合成数据有助于联邦学习应对数据可用性受限的挑战，并支持鲁棒模型的开发。此外，我们分析了生成式人工智能在联邦学习中的多种应用，这些应用能够实现更加个性化的解决方案。