Know Your Data to Harness Federated Machine Learning

Nowadays, deploying artificial intelligence no longer guarantees a competitive edge. What truly sets companies apart is access to diverse, extensive, high-quality data that enhances their AI system’s performance compared with that of their competitors. But concerns over data privacy can limit the use of unique, relevant data for analysis.

This problem can be alleviated by means of privacy-preserving federated learning. This technique, in combination with a special type of encryption, enables an AI model or any other type of algorithm to be trained using data from multiple, decentralized servers controlled by different organizations — all while respecting the privacy of the individuals or organizations whose data is being used for the training.1 Simply put, federated learning entails sending the algorithm to the data rather than sending the data to the algorithm.

This is how Switzerland-based Zurich Insurance Group was able to improve a predictive algorithm with data from Orange, a British telecommunications company. Using a commercial federated learning platform, Zurich’s algorithm could be trained, and its predictive capabilities improved, without the need for Orange to release any data. The collaboration led to a 30% improvement in the AI system’s predictions, which translated into a significant revenue increase for Zurich. For Orange, it represented a new way of monetizing its data while still preserving its privacy.

Federated Learning Across and Within Industries

Real applications of federated learning are now rapidly emerging as organizations search for more data on which to train the AI systems they hope will deliver competitive advantage. For example, a large bank’s credit unit used the approach to fine-tune its algorithm for predicting loan defaults, using data owned by one of the largest global telecommunications companies, and improved prediction accuracy by about 10%.

The value of such collaborations stems from the ability to train AI systems on much richer data sets than any one organization could assemble on its own. To do this, organizations need to identify partners whose data could be used in a federated learning approach to improve their AI systems’ performance.

While it might appear to be more logical for organizations from different industries to collaborate with one another than companies in the same industry, federated learning can facilitate cooperation within industries, including between direct competitors.

Certain pathology departments within competing private hospitals that have struggled to compile robust data sets on their own are doing this.

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