Emerging trends in federated learning: from model fusion to federated X learning

• Published in: International journal of machine learning and cybernetics Vol. 15; no. 9; pp. 3769 - 3790
• Main Authors: Ji, Shaoxiong, Tan, Yue, Saravirta, Teemu, Yang, Zhiqin, Liu, Yixin, Vasankari, Lauri, Pan, Shirui, Long, Guodong, Walid, Anwar
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2024; Springer Nature B.V
• Subjects: Adaptive algorithms; Algorithms; Artificial Intelligence; Bayesian analysis; Communication; Complex Systems; Computational Intelligence; Control; Engineering; Federated learning; Informatics; Machine learning; Mechatronics; Original Article; Paradigms; Pattern Recognition; Privacy; Regularization; Regularization methods; Robotics; State-of-the-art reviews; Systems Biology; Taxonomy; Trends; Unsupervised learning; Model fusion; Learning algorithms; Federated learning

Federated learning is a new learning paradigm that decouples data collection and model training via multi-party computation and model aggregation. As a flexible learning setting, federated learning has the potential to integrate with other learning frameworks. We conduct a focused survey of federate...