Federated Reinforcement Distillation with Proxy Experience Memory
By Han Cha, Jihong Park, Hyesung Kim, Seong-Lyun Kim, Medhi Bennis. 2019
19 citations recorded by the source corpus.
This paper is presented at 28th International Joint Conference on Artificial Intelligence (IJCAI-19) 1st Wksp. Federated Machine Learning for User Privacy and Data Confidentiality (FML'19), Macau, August 2019.
Subfields: Artificial Intelligence
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Medhi Bennis
- Jihong Park
- Anis Elgabli
- Sumudu Samarakoon
- Merouane Debbah
- Seong-Lyun Kim
- Hyesung Kim
- Seungeun Oh
Citations in the corpus
- Wireless Network Intelligence at the Edge (2018, methodology): “Here, n is the sample complexity to satisfy a target generalization error with a target reliability 1 − ε. PAC Framework Applications: (a) PAC-VC Bound: If the hypothesis space H is infinite, e.g., in deep NNs or non-parametric models such as GP, one can derive the GE using the VapnikChervonenkis (V”
- Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications (2020, background): “The number of these generated proxy samples or representations can further be oversampled by mixing them across different workers [146] and/or remixing the mixed-up samples or representations [72].”
- Distilling On-Device Intelligence at the Network Edge (2019, methodology): “In these ways, FD yields comparable accuracy with Vanilla FL, while reducing the communication overhead by tenfold, in our experiments with reinforcement learning for completing the CartPole game [11] and supervised learning for classifying the MNIST dataset [9].”