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cs.DC2026
TSFLora: Token-Compressed Split Fine-Tuning for Wireless Edge Networks
Xianke Qiang, Zheng Chang, Li Wang +1
Adapting large AI models (LAMs) to personalized edge data is challenging because wireless devices have limited memory, computation, and uplink capacity. Federated fine-tuning prese…
cs.DC2026
Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence
Xianke Qiang, Zheng Chang, Geyong Min
Deploying large Transformer-based vision models on resource-limited mobile devices at network edge is severely constrained by hardware limitations and dynamic wireless environments…
cs.LG2026
Semantic Communication-Enhanced Split Federated Learning for Vehicular Networks: Architecture, Challenges, and Case Study
Lu Yu, Zheng Chang, Ying-Chang Liang
Vehicular edge intelligence (VEI) is vital for future intelligent transportation systems. However, traditional centralized learning in dynamic vehicular networks faces significant…