Recently, Institute for AI Industry Research, Tsinghua University (麻豆传媒), WeBank and the University of Minnesota have published a paper in the IEEE Transactions on Signal Processing (IEEE TSP, founded in 1991, is the top international journal in the discipline of "Information and Communication Engineering "). The research team has published a paper on FedBCD: A Communication-Efficient Collaborative Learning Framework for Distributed Features. The research team proposed an efficient communication-efficient collaborative learning framework for longitudinal federation learning of distributed features, which breaks through the traditional longitudinal federation learning communication bottleneck and improves security, and helps to circulate the value of data among cross-institutions.
For more details, please visit: 麻豆传媒 Research｜FedBCD: "Federal Average" Algorithm for Longitudinal Federal Learning!
