MIT Sloan Management Review Article on Know Your Data to Harness Federated Machine Learning

A collaborative approach to training AI models can yield better results, but it requires finding partners with data that complements your own.

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.

About the Author

José Parra-Moyano is a professor of digital strategy at the International Institute for Management Development (IMD) in Lausanne, Switzerland. Karl Schmedders is a professor of finance at IMD in Lausanne. Maximilian Werner is an associate director and research fellow with IMD’s Venture Asset Management initiative in Lausanne.

Learn more about MIT SMR.