Federated Learning (FL) enables collaborative model training across diverse
entities while safeguarding data privacy. However, FL faces challenges such as
data heterogeneity and model diversity. The Meta-Federated Learning (Meta-FL)
framework has been introduced to tackle these challenges. Meta-FL employs an
optimization-based Meta-Aggregator to navigate the complexities of
heterogeneous model updates. The Meta-Aggregator enhances the global model's
performance by leveraging meta-features, ensuring a tailored aggregation that
accounts for each local model's accuracy. Empirical evaluation across four
healthcare-related datasets demonstrates the Meta-FL framework's adaptability,
efficiency, scalability, and robustness, outperforming conventional FL
approaches. Furthermore, Meta-FL's remarkable efficiency and scalability are
evident in its achievement of superior accuracy with fewer communication rounds
and its capacity to manage expanding federated networks without compromising
performance.

元联邦学习通过优化的元汇聚器导航异构模型更新的复杂性，利用元特征增强了全局模型的性能，从而保证了对每个本地模型准确性的个性化汇聚。在四个与医疗相关的数据集上进行的实证评估显示，Meta-FL 框架具有适应性、效率、可扩展性和鲁棒性，优于传统的联邦学习方法。此外，Meta-FL 在较少的通信轮次中实现了优越的准确性，并且能够在不影响性能的情况下管理不断扩大的联邦网络，显示出卓越的效率和可扩展性。