Meta-FL: A Novel Meta-Learning Framework for Optimizing Heterogeneous Model Aggregation in Federated Learning
<|reference_start|>Meta-FL: A Novel Meta-Learning Framework for Optimizing Heterogeneous Model Aggregation in Federated Learning: 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.<|reference_end|>
@article{alsulaimawi2024meta-fl:,
    title={Meta-FL: A Novel Meta-Learning Framework for Optimizing Heterogeneous
  Model Aggregation in Federated Learning},
    author={Zahir Alsulaimawi},
    journal={arXiv preprint arXiv:2406.16035},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.16035},
    primaryClass={cs.LG cs.CR}
}