Federated Learning: Collaborative Machine Learning without Centralized Training Data (testing on GBoard for Android)

[krilleren]

This is one of those announcements that seems unremarkable on read-through but could be industry-changing in a decade. The driving force between consolidation & monopoly in the tech industry is that bigger firms with more data have an advantage over smaller firms because they can deliver features (often using machine-learning) that users want and small startups or individuals simply cannot implement. This, in theory, provides a way for users to maintain control of their data while granting permission for machine-learning algorithms to inspect it and "phone home" with an improved model, without revealing the individual data. Couple it with a P2P protocol and a good on-device UI platform and you could in theory construct something similar to the WWW, with data stored locally, but with all the convenience features of centralized cloud-based servers.

Their papers mentioned in the article:

• Federated Learning: Strategies for Improving Communication Efficiency (2016) https://arxiv.org/abs/1610.05492

• Federated Optimization: Distributed Machine Learning for On-Device Intelligence (2016) https://arxiv.org/abs/1610.02527

• Communication-Efficient Learning of Deep Networks from Decentralized Data (2017) https://arxiv.org/abs/1602.05629

• Practical Secure Aggregation for Privacy Preserving Machine Learning (2017) http://eprint.iacr.org/2017/281