Snake Learning: A Communication- and Computation-Efficient Distributed Learning Framework for 6G

no code implementations • 6 May 2024 • Xiaoxue Yu, Xingfu Yi, Rongpeng Li, Fei Wang, Chenghui Peng, Zhifeng Zhao, Honggang Zhang

Existing distributed learning frameworks like Federated Learning and Split Learning often struggle with significant challenges in dynamic network environments including high synchronization demands, costly communication overheads, severe computing resource consumption, and data heterogeneity across network nodes.

Federated Learning Language Modelling +1

RHFedMTL: Resource-Aware Hierarchical Federated Multi-Task Learning

no code implementations • 1 Jun 2023 • Xingfu Yi, Rongpeng Li, Chenghui Peng, Fei Wang, Jianjun Wu, Zhifeng Zhao

The rapid development of artificial intelligence (AI) over massive applications including Internet-of-things on cellular network raises the concern of technical challenges such as privacy, heterogeneity and resource efficiency.

Federated Learning Multi-Task Learning