Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectives

In recent years, the barriers to the development of Artificial Intelligence (AI) have been broken down with the rapid progress of ABC technologies in computing: AI, Big Data, and Cloud Computing, as well as the emergence of cost-effective specialized hardware [213] and software [98]. This has led to the world entering the third wave of AI development: Deep Learning [117]. The success of current data-driven AI relies on massive amounts of training data and follows a gather-and-analyze paradigm [233], which confronts with challenges of complying with rigorous data protection regulations such as OECD Privacy Guidelines [217] and General and Data Protection Regulation (GDPR) [223]. So although data-centric AI is currently mainstream paradigm, Federated Learning [132], a novel model-centric distributed collaborative training framework, is gaining popularity in both academia and industry for its advantages in complying with privacy regulations [219]. According to the definitions of IEEE Standard for Federated Machine Learning (FML, aka FL) [205], FL is a framework or system that enables multiple participants to collaboratively build and use machine learning models without disclosing the raw and private data owned by the participants while achieving good performance. For example, a typical workflow of FL systems is that the entity with modeling demand (aka FL server) first deploys the FL services and initializes the model training task, and then distributing this task to participants with training data (aka FL clients) for modeling [13]. Based on this workflow pattern, many FL frameworks have been derived with specialized improvements in communication [111, 161, 240], optimizaiton [107, 129, 133], robustness [44, 124, 198] and privacy [14, 32, 62]. While these fascinating improvements greatly enhance the utility of FL, they all follow a task-based interaction paradigm, in which an FL server dominates the cooperation between FL participants. In this narrow interpretation of FL, the data owner is treated more like a worker than a collaborator and performs training primarily for the benefit of the server's goals.