المفاهيم الأساسية
AutoFLIPと呼ばれる新しいフェデレーテッドラーニング手法は、損失探索を通じてモデルプルーニングを自動化し、リソースの制約があるクライアントにおける深層学習モデルの効率性と精度を向上させます。
Interno, C., Raponi, E., van Stein, N., Bäck, T., Olhofer, M., Jin, Y., & Hammer, B. (2024). Adaptive Hybrid Model Pruning in Federated Learning through Loss Exploration. arXiv preprint arXiv:2405.10271v2.
This paper introduces AutoFLIP, a novel approach for adaptive hybrid model pruning in federated learning (FL) that leverages loss exploration to optimize deep learning models for resource-constrained clients. The study aims to address the challenges of high communication costs, computational constraints, and non-IID data distributions in FL.