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cs.LG2026
SL-FAC: A Communication-Efficient Split Learning Framework with Frequency-Aware Compression
Zehang Lin, Miao Yang, Haihan Zhu +9
The growing complexity of neural networks hinders the deployment of distributed machine learning on resource-constrained devices. Split learning (SL) offers a promising solution by…
cs.LG2025
Generative Feature Imputing -- A Technique for Error-resilient Semantic Communication
Jianhao Huang, Qunsong Zeng, Hongyang Du +1
Semantic communication (SemCom) has emerged as a promising paradigm for achieving unprecedented communication efficiency in sixth-generation (6G) networks by leveraging artificial…
cs.LG2024
LoLaFL: Low-Latency Federated Learning via Forward-only Propagation
Jierui Zhang, Jianhao Huang, Kaibin Huang
Federated learning (FL) has emerged as a widely adopted paradigm for enabling edge learning with distributed data while ensuring data privacy. However, the traditional FL with deep…