KDD 2026 - Efficient and Differentially Private Federated LLM Fine-Tuning on Heterogeneous Clients @TheOfficialACM

KDD 2026 - Efficient and Differentially Private Federated LLM Fine-Tuning on Heterogeneous Clients @TheOfficialACM

Uploaded July 2026 | Updated September 2026, 2 weeks ago

Nan Yan, Yuqing Li, Xiong Wang, Jing Chen, Wei Wang, Kun He, Ruiying Du, Shuhua Li

KDD 2026 - Efficient and Differentially Private Federated LLM Fine-Tuning on Heterogeneous Clients

KDD 2026-Combating Web-Based E-Comm. Fraud Syndicates: Fairness-AwareHypergraph Contr. Fraud Detect.

KDD 2026 - Perturbation Effects on Robustness and Individual Fairness

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Universal Designs in Public Spaces

KDD2026-CASH3D:Color-aware3DMultimodal Hypergraph Learning for Revealing Bio. Meaningful Tissue Org.

KDD 2026 - DEFINED: A Data-Efficient Computational Framework for Fine-Grained Creativity Assessment

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KDD 2026 - Invariant-Stratified Propagation for Expressive Graph Neural Networks

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KDD 2026 - A Geometric Information Bottleneck for Activation Steering

Association for Computing Machinery (ACM) |

KDD 2026 - Efficient and Differentially Private Federated LLM Fine-Tuning on Heterogeneous Clients