Workshop on Distributed and Private Machine Learning

Over the last decade, progress in machine learning has resulted in a surge of data-driven services affecting our daily lives. Conversational agents, healthcare providers, online retailers, and social networks continually access and jointly process vast amounts of data about their geographically distributed customers. Progress in distributed machine learning technology which has enabled widespread adoption and personalization has also raised issues regarding privacy, accountability, and fairness. This tension is particularly apparent in the context of the Covid-19 pandemic. This motivates the need to jointly address distributed and private machine learning technologies.