Clustered Federated Multi-Task Learning: A Communication-and-Computation Efficient Sparse Sharing Approach

Federated multi-task learning (FMTL) is a promising technology to tackle one of the most severe non-independent and identically distributed (non-IID) data challenge in federated learning (FL), which treats each client as a single task and learns personalized models by exploiting task correlations. H...

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Vydané v:	IEEE transactions on wireless communications Ročník 24; číslo 6; s. 4824 - 4838
Hlavní autori:	Ai, Yuhan, Chen, Qimei, Zhu, Guangxu, Wen, Dingzhu, Jiang, Hao, Zeng, Jun, Li, Ming
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.06.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Adaptation models Algorithms bandwidth allocation Bandwidths Channel allocation Clients clustering Communication Computation Computational modeling Convergence Correlation Customization Data models Federated learning Federated multi-task learning model pruning Multitasking Optimization sparse sharing Sparsity Training Wireless communication
ISSN:	1536-1276, 1558-2248
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