Contribution and Fairness-Aware Federated Learning [Han Yu]

Federated Learning (FL) is an emerging area of AI focusing on training machine learning models in a privacy-preserving manner. The success of FL, especially in open collaboration settings, rests on being able to continuously attract high quality data owners to participate. This, at the same time, also opens the FL to adversaries trying to exploit other parties’ sensitive privacy information. It is important to adopt an ecosystem management approach to building trust and controlling risk in FL. In this talk, I will share with you some attempts we made at the Trustworthy Federated Ubiquitous Learning (TrustFUL) Research Lab in this general direction, including data valuation under FL settings, fair treatment for FL participants, and studying user reactions to incentive schemes developed for federated learning.