Thermo-FL: Thermal-Aware Robust Federated Fine-Tuning of Large Language Models for Edge AI
Federated fine-tuning enables large language models to adapt on edge devices without centralizing private data, but practical deployments must address hardware instability and adversarial update corruption together. Thermally constrained clients may throttle, slow local training, or delay synchronous aggregation, while Byzantine clients and communication-layer adversaries can corrupt the updates used to form the global model. To address these challenges, we present Thermo-FL, a thermal-aware federated LoRA fine-tuning framework that uses device temperature as an active control signal for local
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- LinkedLinked via arxiv author · 85%Shiva Shrestha →
“Thermo-FL: Thermal-Aware Robust Federated Fine-Tuning of Large Language Models for Edge AI”
- LinkedLinked via arxiv author · 85%Kazi Shaharair Sharif →
“Thermo-FL: Thermal-Aware Robust Federated Fine-Tuning of Large Language Models for Edge AI”
- LinkedLinked via arxiv author · 85%Zongxing Xie →
“Thermo-FL: Thermal-Aware Robust Federated Fine-Tuning of Large Language Models for Edge AI”
- LinkedLinked via arxiv author · 85%Jiajing Huang →
“Thermo-FL: Thermal-Aware Robust Federated Fine-Tuning of Large Language Models for Edge AI”
- LinkedLinked via arxiv author · 85%Anhao Xiang →
“Thermo-FL: Thermal-Aware Robust Federated Fine-Tuning of Large Language Models for Edge AI”
- LinkedLinked via arxiv author · 85%Honghui Xu →
“Thermo-FL: Thermal-Aware Robust Federated Fine-Tuning of Large Language Models for Edge AI”