TIGER turns the low-rank attention gradient subspace into a differentiable objective for continuous embedding optimization, improving reconstruction quality and robustness over prior discrete token tests especially under noise or DP.
Flowertune: A cross-domain benchmark for federated fine-tuning of large language models
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Federated PEFT on LLMs across healthcare and finance datasets performs close to centralized training and beats isolated local training under non-IID conditions.
Federated QLoRA fine-tuning on distributed PA manuals from SIGESON and SIDFORS yields ROUGE-1/2/L of 61.10/55.77/59.44 and BLEU-4 of 45.02, close to centralized training.
citing papers explorer
-
TIGER: Inverting Transformer Gradients via Embedding-Subspace Distance Optimization
TIGER turns the low-rank attention gradient subspace into a differentiable objective for continuous embedding optimization, improving reconstruction quality and robustness over prior discrete token tests especially under noise or DP.
-
Towards the Next Frontier of LLMs, Training on Private Data: A Cross-Domain Benchmark for Federated Fine-Tuning
Federated PEFT on LLMs across healthcare and finance datasets performs close to centralized training and beats isolated local training under non-IID conditions.
-
GuidaPA: Privacy-Preserving Chatbot for Public Administration via Federated Learning
Federated QLoRA fine-tuning on distributed PA manuals from SIGESON and SIDFORS yields ROUGE-1/2/L of 61.10/55.77/59.44 and BLEU-4 of 45.02, close to centralized training.