  Machine Learning (ML) techniques have begun to dominate data analytics
applications and services. Recommendation systems are a key component of online
service providers. The financial industry has adopted ML to harness large
volumes of data in areas such as fraud detection, risk-management, and
compliance. Deep Learning is the technology behind voice-based personal
assistants, etc. Deployment of ML technologies onto cloud computing
infrastructures has benefited numerous aspects of our daily life. The
advertising and associated online industries in particular have fuelled a rapid
rise the in deployment of personal data collection and analytics tools.
Traditionally, behavioural analytics relies on collecting vast amounts of data
in centralised cloud infrastructure before using it to train machine learning
models that allow user behaviour and preferences to be inferred. A contrasting
approach, distributed data analytics, where code and models for training and
inference are distributed to the places where data is collected, has been
boosted by two recent, ongoing developments: increased processing power and
memory capacity available in user devices at the edge of the network, such as
smartphones and home assistants; and increased sensitivity to the highly
intrusive nature of many of these devices and services and the attendant
demands for improved privacy. Indeed, the potential for increased privacy is
not the only benefit of distributing data analytics to the edges of the
network: reducing the movement of large volumes of data can also improve energy
efficiency, helping to ameliorate the ever increasing carbon footprint of our
digital infrastructure, enabling much lower latency for service interactions
than is possible when services are cloud-hosted. These approaches often
introduce challenges in privacy, utility, and efficiency trade-offs, while
having to ensure fruitful user engagement.
