Federated Learning (FL) has recently emerged as the de facto framework for distributed machine learning (ML) that preserves the privacy of data, especially in the proliferation of mobile and edge devices with their increasing capacity for storage and computation. To fully utilize the vast amount of geographically distributed, diverse and privately owned data that is stored across these devices, FL provides a platform on which local devices can build their own local models whose training processes can be synchronized via sharing differential parameter updates. This was done without exposing their private training data, which helps mitigate the risk of privacy violation, in light of recent policies such as the General Data Protection Regulation (GDPR). Such potential use of FL has since then led to an explosive attention from the ML community resulting in a vast, growing amount of both theoretical and empirical literature that push FL so close to being the new standard of ML as a democratized data analytic service.

Interestingly, as FL comes closer to being deployable in real-world scenarios, it also surfaces a growing set of challenges on trustworthiness, fairness, auditability, scalability, robustness, security, privacy preservation, decentralizability, data ownership and personalizability that are all becoming increasingly important in many interrelated aspects of our digitized society. Such challenges are particularly important in economic landscapes that do not have the presence of big tech corporations with big data and are instead driven by government agencies and institutions with valuable data locked up or small-to-medium enterprises & start-ups with limited data and little funding. With this forethought, the workshop envisions the establishment of an AI ecosystem that facilitates data and model sharing between data curators as well as interested parties in the data and models while protecting personal data ownership.

Poster Session: [ protected link dropped ]