each learn their own set of rules based on the input data points • The output of one box is the input for the next box in the chain • Capable of constructing higher level patterns from low level patterns by way of composition
Roch, Massimo & Martina, Maurizio. (2019). Edge Computing: A Survey On the Hardware Requirements in the Internet of Things World. Future Internet. 11. 100. 10.3390/fi11040100.
• Federated learning systems rely on a central server to coordinate the model training process on multiple, distributed client devices where the data are stored • The model training is performed locally on the client devices, without moving the data out of the client devices
Pyrlangpoc.Stack do use Agent def start_link(_) do # __MODULE__ Agent.start_link(fn -> %{} end, name: __MODULE__) end def put(key, value) do Agent.update(__MODULE__, &Map.put(&1, key, value)) end def get(key) do Agent.get(__MODULE__, &Map.get(&1, key)) end end
Arcas, B.A.Y. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017; Singh, A.; Zhu, J., Eds.; Volume 54, PMLR, Fort Lauderdale, FL, USA; pp. 1273–1282
Systematic Literature Review on Federated Machine Learning.” ACM Computing Surveys (CSUR) 54 (2021): 1 - 39. • McMahan, B.; Moore, E.; Ramage, D.; Hampson, S.; Arcas, B.A.Y. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017; Singh, A.; Zhu, J., Eds.; Volume 54, PMLR, Fort Lauderdale, FL, USA; pp. 1273–1282 • D. Ye, R. Yu, M. Pan, and Z. Han. 2020. Federated Learning in Vehicular Edge Computing: A Selective Model Aggregation Approach. IEEE Access (2020), 1–1. • Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. 2018. Federated Optimization in Heterogeneous Networks. arXiv:1812.06127 [cs.LG]