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cs.LG2026
Learning to Route LLMs from Implicit Cost-Performance Preferences via Meta-Learning
Jiahao Zeng, Ming Tang, Ningning Ding
Large language models (LLMs) present a trade-off between performance and cost, where more powerful models incur greater expense. LLM routing aims to mitigate expenses while maintai…
cs.LG2026
ASFL: An Adaptive Model Splitting and Resource Allocation Framework for Split Federated Learning
Chuiyang Meng, Ming Tang, Vincent W. S. Wong
Federated learning (FL) enables multiple clients to collaboratively train a machine learning model without sharing their raw data. However, the limited computation resources of the…
cs.LG2026
ZorBA: Zeroth-order Federated Fine-tuning of LLMs with Heterogeneous Block Activation
Chuiyang Meng, Ming Tang, Vincent W. S. Wong
Federated fine-tuning of large language models (LLMs) enables collaborative tuning across distributed clients. However, due to the large size of LLMs, local updates in federated le…