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arXiv Machine Learning By Shiva Shrestha, Kazi Shaharair Sharif, Zongxing Xie, Jiajing Huang, Anhao Xiang, Honghui Xu

Thermo-FL: Thermal-Aware Robust Federated Fine-Tuning of Large Language Models for Edge AI

Read the original on arXiv Machine Learning →

Thermo-FL is a thermal‑aware federated LoRA fine‑tuning framework that adapts local adapter training and sparse update transmission based on device temperature. It introduces TERRA, a robust aggregation pipeline that uses norm filtering, mask‑aware directional validation, adaptive clipping, and mask‑aware aggregation to defend against Byzantine and communication‑layer adversaries. Experiments on a large‑scale emulator and a Jetson testbed show Thermo-FL improves robustness under adversarial sparse aggregation, stabilizes device temperature, reduces upload size, and preserves utility on GSM8K and BoolQ tasks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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