Communications of the ACM
Federated Learning for Privacy-Preserving AI
Credit: Andrij Borys Associates, Shutterstock
There has been remarkable success of machine learning (ML) technologies in empowering practical artificial intelligence (AI) applications, such as automatic speech recognition and computer vision. However, we are facing two major challenges in adopting AI today. One is that data in most industries exist in the form of isolated islands. The other is the ever-increasing demand for privacy-preserving AI. Conventional AI approaches based on centralized data collection cannot meet these challenges. How to solve the problem of data fragmentation and isolation while complying with the privacy-protection laws and regulations is a major challenge for AI researchers and practitioners.
On the legal front, lawmakers and regulatory bodies are coming up with new laws ruling how data shall be managed and used.3 One prominent example is the adoption of the General Data Protection Regulation (GDPR) by the European Union in 2018. In the United States, the California Consumer Privacy Act will be enacted in 2020. China's Cyber Security Law, came into effect in 2017, also imposed strict controls on data collection and transactions.
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