privacy-preserving

3 stories

Artificial Intelligence #federated learning#lora

SDFLoRA: Selective Decoupled Federated LoRA for Privacy-Preserving Fine-Tuning with Heterogeneous Clients

Federated learning for LLMs faces challenges from heterogeneous client ranks and data distributions. SDFLoRA proposes a structure-aware LoRA framework that decouples updates into shared and private components, enabling stable aggregation, personalization, and improved differential privacy. Experiments show it outperforms existing federated LoRA baselines.

Jun 16, 2026 1 source

AL-GNN: New Privacy-Preserving Continual Graph Learning Eliminates Replay Buffers and Backpropagation

Technology

Artificial Intelligence #graph neural networks#continual learning

AL-GNN: New Privacy-Preserving Continual Graph Learning Eliminates Replay Buffers and Backpropagation

Researchers propose AL-GNN, a continual graph learning framework that uses analytic learning to avoid replay buffers and backpropagation. It achieves 10% higher average performance on CoraFull, reduces forgetting by over 30% on Reddit, and cuts training time by nearly 50% while preserving data privacy.

Jun 16, 2026 1 source

Privacy-Preserving Text Sanitization for Distributed Agents via Disentangled Representations

Technology

Artificial Intelligence #privacy-preserving#text sanitization

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.

Jun 16, 2026 1 source