Scalable and Resource-Efficient Second-Order Federated Learning via Over-the-Air Aggregation
<|reference_start|>Scalable and Resource-Efficient Second-Order Federated Learning via Over-the-Air Aggregation: Second-order federated learning (FL) algorithms offer faster convergence than their first-order counterparts by leveraging curvature information. However, they are hindered by high computational and storage costs, particularly for large-scale models. Furthermore, the communication overhead associated with large models and digital transmission exacerbates these challenges, causing communication bottlenecks. In this work, we propose a scalable second-order FL algorithm using a sparse Hessian estimate and leveraging over-the-air aggregation, making it feasible for larger models. Our simulation results demonstrate more than $67\%$ of communication resources and energy savings compared to other first and second-order baselines.<|reference_end|>
@article{ghalkha2024scalable,
    title={Scalable and Resource-Efficient Second-Order Federated Learning via
  Over-the-Air Aggregation},
    author={Abdulmomen Ghalkha, Chaouki Ben Issaid, and Mehdi Bennis},
    journal={arXiv preprint arXiv:2410.07662},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.07662},
    primaryClass={cs.LG}
}