发表机构
Kennesaw State University（肯尼索州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Thermo-FL是热感知联邦LoRA微调框架，通过客户端温度调控和服务器TERRA鲁棒聚合，提升边缘LLM在热应力与攻击下的鲁棒性，稳定温度并保留任务性能。
AI 中文摘要
联邦微调可让大语言模型在边缘设备上进行适配，无需集中私有数据，但实际部署必须同时应对硬件不稳定和更新被恶意篡改的问题。受温度限制的客户端可能会降速、减缓本地训练或延迟同步聚合，而拜占庭客户端和通信层攻击者可能会篡改用于构建全局模型的更新。为应对这些挑战，我们提出了Thermo-FL，这是一个热感知联邦LoRA微调框架，它将设备温度作为主动控制信号，用于本地适配器训练和稀疏更新传输。在客户端，Thermo-FL会根据设备升温或降温调整活跃LoRA层的比例和传输更新的密度，从而在热应力下减少工作负载。在服务器端，Thermo-FL引入了TERRA，这是一种用于处理动态稀疏LoRA更新的鲁棒聚合流水线，结合了范数过滤、掩码感知方向验证、自适应活跃坐标裁剪和掩码感知聚合。我们使用大规模模拟器和基于Jetson的物理测试平台对Thermo-FL进行了评估。在模拟器中，Thermo-FL在对抗性稀疏聚合下提高了鲁棒性，在干净和攻击场景中均实现了最强的BoolQ准确率，同时在GSM8K上保持了竞争力。在物理原型中，Thermo-FL稳定了设备温度，通过位图稀疏编码减少了压缩上传大小，并在符号翻转/缩放和MITM扰动下保留了GSM8K的效用。这些结果表明，安全的边缘LLM适配应综合考虑硬件行为、工作负载调节、稀疏通信和聚合鲁棒性。
英文摘要
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 adapter training and sparse update transmission. On the client side, Thermo-FL adjusts the active LoRA-layer fraction and transmitted update density as devices heat or cool, reducing workload under thermal stress. On the server side, Thermo-FL introduces TERRA, a robust aggregation pipeline for dynamically sparse LoRA updates that combines norm filtering, mask-aware directional validation, adaptive active-coordinate clipping, and mask-aware aggregation. We evaluate Thermo-FL using both a large-scale emulator and a Jetson-based physical testbed. In the emulator, Thermo-FL improves robustness under adversarial sparse aggregation and achieves the strongest BoolQ accuracy across clean and attack settings while remaining competitive on GSM8K. In the physical prototype, Thermo-FL stabilizes device temperature, reduces compressed upload size through bitmap sparse encoding, and preserves GSM8K utility under sign-flip/scale and MITM perturbations. These results show that secure edge LLM adaptation should jointly consider hardware behavior, workload regulation, sparse communication, and aggregation robustness.
