Self-Balancing Federated Learning With Global Imbalanced Data in Mobile Systems

• Published in: IEEE transactions on parallel and distributed systems Vol. 32; no. 1; pp. 59 - 71
• Main Authors: Duan, Moming, Liu, Duo, Chen, Xianzhang, Liu, Renping, Tan, Yujuan, Liang, Liang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Balancing; Communications traffic; Data augmentation; Data models; Datasets; Distributed databases; distributed machine learning; Electronic devices; Federated learning; Machine learning; Mobile communication systems; Mobile handsets; Neural networks; Privacy; Rescheduling; Servers; Training

Federated learning (FL) is a distributed deep learning method that enables multiple participants, such as mobile and IoT devices, to contribute a neural network while their private training data remains in local devices. This distributed approach is promising in the mobile systems where have a large...