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| 연합 학습× | 지식 증류× | |
|---|---|---|
| 분야≠ | 프라이버시 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2017 | 2015 |
| 창시자≠ | McMahan et al. | Hinton, G., Vinyals, O. & Dean, J. |
| 유형≠ | Distributed privacy-preserving machine learning | Neural network compression (teacher–student) |
| 원전≠ | McMahan, B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. Artificial Intelligence and Statistics, 1273–1282. link ↗ | Hinton, G., Vinyals, O. & Dean, J. (2015). Distilling the Knowledge in a Neural Network. NeurIPS Deep Learning Workshop. link ↗ |
| 별칭 | Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme | Bilgi Damıtma (Knowledge Distillation), bilgi damıtma, teacher-student distillation, model distillation |
| 관련≠ | 3 | 5 |
| 요약≠ | Federated Learning is a distributed machine learning paradigm introduced by McMahan et al. in 2017 in which a global model is trained collaboratively across multiple decentralized clients — such as mobile devices or hospital systems — without ever transferring raw data to a central server. Each participant computes model updates locally using its private data; only those updates, not the underlying data, are communicated and aggregated by the server to improve the shared model. | Knowledge Distillation is a model-compression technique, introduced by Geoffrey Hinton and colleagues in 2015, that trains a small student model using the soft-label outputs of a large teacher model. Distilled models such as DistilBERT and TinyBERT reach roughly 97% of the larger model's performance while running far faster. |
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