Four short links: 13 February 2019


Federated Learning, Clever-Commit, Web Design Trends, and Social Context

  1. Towards Federated Learning at Scale — research paper from Google on a distributed machine learning approach which enables training on a large corpus of decentralized data residing on devices like mobile phones. They’re working on it for Android; first app is the keyboard: Our system enables one to train a deep neural network, using TensorFlow, on data stored on the phone which will never leave the device. The weights are combined in the cloud with Federated Averaging, constructing a global model which is pushed back to phones for inference. An implementation of Secure Aggregation ensures that on a global level, individual updates from phones are uninspectable. The system has been applied in large-scale applications, for instance in the realm of a phone keyboard.
  2. Mozilla’s Clever-CommitBy combining data from the bug-tracking system and the version-control system (aka, changes in the code base), Clever-Commit uses artificial intelligence to detect patterns of programming mistakes based on the history of the development of the software. This allows us to address bugs at a stage when fixing a bug is a lot cheaper and less time consuming than upon release. Video.
  3. SaaS Web Design Trends — everything from where the logo is to what action is being called for to the rise of custom illustrations (versus photographs).
  4. The Role of Social Context for Fake News DetectionIn this paper, we study the novel problem of exploiting social context for fake news detection. We propose a tri-relationship embedding framework TriFN, which models publisher-news relations and user-news interactions simultaneously for fake news classification. We conduct experiments on two real-world data sets, which demonstrate that the proposed approach significantly outperforms other baseline methods for fake news detection. (via Paper a Day)

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