Topic
federated learning
Privacy-Preserving Text Sanitization for Distributed Agents via Disentangled Representations
Researchers propose DiSan, a privacy-preserving text sanitization framework that uses disentangled representations to separate task semantics from style identifiers. Experiments show it reduces personally identifiable information exposure by 20 times while maintaining 83% answer faithfulness on a multi-agent RAG benchmark, outperforming token-level masking.
PreLort: Prefix-Nested LoRA Enables Federated Fine-Tuning Across Heterogeneous Hardware Ranks
A new method called PreLort addresses the challenge of aggregating federated LoRA adapters with different ranks due to heterogeneous hardware. By organizing adapter dimensions into a prefix hierarchy and introducing segment-wise aggregation and prefix-nested training, PreLort consistently outperforms existing heterogeneous federated LoRA methods in accuracy and ROUGE-L while achieving lower perplexity.
LLM-Encoded Knowledge Guides Federated Graph Recommendation to Improve Accuracy
Researchers propose a federated graph recommendation framework that leverages LLM-encoded semantic knowledge to guide cross-client structural aggregation, addressing the challenge of non-IID client data. The method consistently outperforms existing federated graph baselines on standard benchmarks.