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[ANNOUNCE] Refreshed Research from Cloudera Fast Forward: Semantic Image Search and Federated Learning

[ANNOUNCE] Refreshed Research from Cloudera Fast Forward: Semantic Image Search and Federated Learning

Cloudera Employee


Cloudera Fast Forward are happy to share recent updates of two of our older reports, now freely available to all!

Semantic Image Search



We recently revisited the topic of semantic search on image data. We’ve previously studied applications of deep learning to images in our early report Deep Learning for Image Analysis, and our more recent update: Deep Learning for Image Analysis: 2019 edition.

In our most recent research cycle, we explored two critical requirements of semantic search at scale. First, we wrote a review 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 of ConvNet Playground App, and a set of scripts and tutorials for implementing semantic image search on the Cloudera Machine Learning platform.

Federated Learning




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.


In the time since, Federated Learning has only grown in relevance. Numerous startups have cropped up (and some disappeared by acquisition) with Federated Learning as their core technology. Google continues to promote the technology, including for non-machine learning use cases, as in Federated Analytics: Collaborative Data Science without Data Collection. This year saw (what we believe to be) the first conferences with a heavy focus on federated learning, The Federated Learning Conference and the Open Mined Privacy Conference, as well as dedicated workshops at high profile machine learning conferences like ICML and NeurIPS.


OpenMined continues to build a strong community around private machine learning, creating courses and open source tools to lower the barrier-to-entry to federated learning and related privacy enhancing techniques. Alongside those, TensorFlow Federated, IBM’s federated learning library and are extending the tooling ecosystem.


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.