Communication-Efficient Federated Multi-Task Learning with Sparse Sharing

• Published in: 2023 IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) pp. 1 - 6
• Main Authors: Ai, Yuhan, Chen, Qimei, Liang, Yipeng, Jiang, Hao
• Format: Conference Proceeding
• Language: English
• Published: IEEE 05.09.2023
• Subjects: Adaptation models; bandwidth allocation; Channel allocation; client selection; Closed-form solutions; Data models; Federated multi-task learning; Multitasking; Numerical models; Quantization (signal); sparse sharing

Federated multi-task learning (FMTL) is a promising technology to deal with the severe data heterogeneity issue in federated learning (FL), where each client learns individual models locally and the server extracts similar model parameters from the tasks to keep personalization for models of clients...