Graph representation learning plus iterative augmented Lagrangian optimization creates stronger, harder-to-detect model manipulation attacks on federated LLM fine-tuning, cutting global accuracy by up to 26%.
Emerging safety attack and defense in federated instruction tuning of large language models
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FedDetox uses on-device knowledge-distilled classifiers to sanitize toxic data in federated SLM training, preserving safety alignment comparable to centralized baselines.
citing papers explorer
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Graph Representation Learning Augmented Model Manipulation on Federated Fine-Tuning of LLMs
Graph representation learning plus iterative augmented Lagrangian optimization creates stronger, harder-to-detect model manipulation attacks on federated LLM fine-tuning, cutting global accuracy by up to 26%.
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FedDetox: Robust Federated SLM Alignment via On-Device Data Sanitization
FedDetox uses on-device knowledge-distilled classifiers to sanitize toxic data in federated SLM training, preserving safety alignment comparable to centralized baselines.