In our most recent research cycle, we explored two critical requirements of semantic search at scale. First, we wrote areview of strategies for creating semantic representations of images (including supervised, self supervised, and unsupervised methods). Second, we provide an implementation of semantic search using fast approximate nearest neighbor search (with FAISS). We have released an updated version ofConvNet Playground App, and a set of scripts and tutorials for implementing semantic image search on the Cloudera Machine Learning platform.
Two years ago we wrote a research report about Federated Learning. We’re pleased to make the report freely available to everyone. You can read it online here: Federated Learning.
Federated Learning is no panacea. In a privacy setting, decentralized data simply presents a different attack surface to centralized data. Not all applications require or benefit from federation. However, it is an important tool in the private machine learning toolkit.