Introduction to Federated Learning (Research + Frameworks)

In this video we'll explain how Federated learning works, look at the latest research and look at frameworks and datasets, like PySyft, Flower and Tensorflow Federated.

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Links:
FedAvg paper: https://arxiv.org/abs/1602.05629
FedProx paper: https://arxiv.org/abs/1812.06127
q-FedAvg paper: https://arxiv.org/abs/1905.10497
per-FedAvg paper: https://proceedings.neurips.cc/paper/2020/file/24389bfe4fe2eba8bf9aa9203a44cdad-Paper.pdf
Federated Multi-Task Learning: https://arxiv.org/abs/1705.10467
Advances and Open Problems in Federated Learning: https://arxiv.org/abs/1912.04977
List of papers by topic: https://github.com/chaoyanghe/Awesome-Federated-Learning

Tensorflow Federated: https://www.tensorflow.org/federated
PySyft by OpenMined: https://github.com/OpenMined/PySyft
Flower: https://flower.dev/

Differential Privacy video: https://youtu.be/oNSelFJnPaM
Google AI blogpost on Federated Learning: https://ai.googleblog.com/2017/04/federated-learning-collaborative.html

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Timestamps:

0:00 Introduction
01:03 How Federated Learning Works
03:31 FedAvg
05:00 Improvements to FedAvg + Research
05:46 FedProx
06:39 q-FedAvg
07:16 per-FedAvg
08:13 Curated list of papers by topic
09:01 Frameworks and Datasets
10:37 Privacy-Preserving ML

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Видео Introduction to Federated Learning (Research + Frameworks) канала Mukul Rathi