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Ming-Tao Ji

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#edge computing Oct 2026

Feature Map Compression for Split Learning in Mobile Wireless Environment: Theory and Practice

Split Learning (SL) enables collaborative model training by distributing computation between resource-constrained mobile edge devices and a central server. In this distributed framework, mobile edge devices compute the early layers of the model and transmit feature maps to the server. However, due to practical mobile w...

Wen-Xuan Zhou, Zhi-Hao Qu, Baoliu Ye et al. · IEEE Transactions on Mobile... · 0 citations

Aug 2026

Learning to schedule: interference-aware optimization for edge AI inference on shared GPUs

A time-varying integer program to minimize the long-term total cost of the edge AI inference system, including the inference latency, the inference error rate, the query-dispatching communication cost, and the energy consumption, subject to resource and workload constraints is proposed.

Ming-Tao Ji, Hehan Zhao, Lei Jiao et al. · Science China Information Sc... · 0 citations