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Thermo-FL: Thermal-Aware Robust Federated Fine-Tuning of Large Language Models for Edge AI — arXiv:2608.21172

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.21172
License (as archived): CC0-1.0
Archived: 2026-10-18T10:22:15.708Z
SHA-256 of the archived text: 4fc141212704f1ec94635cd8bda103c8de2908a78fe33f2f8a99ebd0e951e719
Stable URL of this page: https://metraipolis.com/documento/dc_9e149db2befd

How to cite

Thermo-FL: Thermal-Aware Robust Federated Fine-Tuning of Large Language Models for Edge AI — arXiv:2608.21172 — arXiv (en), https://arxiv.org/abs/2608.21172 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_9e149db2befd (Metraipolis archive dc_9e149db2befd, sha256 4fc141212704f1ec94635cd8bda103c8de2908a78fe33f2f8a99ebd0e951e719)

BibTeX

@misc{dc9e149db2befd,
  title = {Thermo-FL: Thermal-Aware Robust Federated Fine-Tuning of Large Language Models for Edge AI — arXiv:2608.21172},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.21172},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_9e149db2befd; sha256 4fc141212704f1ec94635cd8bda103c8de2908a78fe33f2f8a99ebd0e951e719},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.21172. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: calcolo e infrastrutture (retro-catalogo arXiv: annunci del 2026-08-24 in cs.DC).
Categorie arXiv: Machine Learning (cs.LG) ; Distributed, Parallel, and Cluster Computing (cs.DC).
Pagina del lavoro: https://arxiv.org/abs/2608.21172 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T10:18:05.600Z dalla pagina https://arxiv.org/catchup/cs.DC/2026-08-24?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).