arXiv AI June 16, 2026 By Zhikang Shen, Jianrong Lu, Haiyuan Wan, Jianhai Chen

SDFLoRA: Selective Decoupled Federated LoRA for Privacy-preserving Fine-tuning with Heterogeneous Clients

arXiv:2601. 11219v3 Announce Type: replace-cross Abstract: Federated learning (FL) for large language models (LLMs) has attracted increasing attention as a privacy-preserving approach for adapting models over distributed data, where parameter-efficient methods such as Low-Rank Adaptation (LoRA) are widely adopted to reduce communication and memory costs.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 16

PreLort: Prefix-Nested LoRA for Federated Fine-Tuning under Rank Heterogeneity

arXiv:2606. 15963v1 Announce Type: cross Abstract: Federated fine-tuning of large language models using parameter-efficient methods such as LoRA enables privacy-preserving adaptation of foundation models.

By Muhammad Waseem, Nurbek Tastan, Andrej Jovanovic, Nicholas D. Lane, Nils Lukas, Karthik Nandakumar, Samuel Horvath

llms fine-tuning safety

Hugging Face Trending Papers

Jun 1

IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning

Heterogeneous Differential Privacy (HDP) in Federated Learning (FL) allows clients to select individual privacy budgets ($\varepsilon_i$) according to institutional policies and data sensitivity. In practice, many HDP-FL systems employ $\varepsilon$-aware server aggregation to improve model utility by re-weighting client updates according to their declared privacy budgets.

safety

arXiv Machine Learning

Jun 2

IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning

arXiv:2606. 02563v1 Announce Type: new Abstract: Heterogeneous Differential Privacy (HDP) in Federated Learning (FL) allows clients to select individual privacy budgets ($\varepsilon_i$) according to institutional policies and data sensitivity.

By Farhin Farhad Riya, Olivera Kotevska, Jinyuan Stella Sun

safety

arXiv Machine Learning

Jun 16

DP-Hype: Federated Differentially Private Hyperparameter Search

arXiv:2510. 04902v3 Announce Type: replace Abstract: Tuning hyperparameters in federated machine learning can substantially impact model performance.

By Johannes Liebenow, Thorsten Peinemann, Esfandiar Mohammadi

benchmarks safety

arXiv AI

Jun 15

FedRot-LoRA: Mitigating Rotational Misalignment in Federated LoRA

arXiv:2602. 23638v3 Announce Type: replace-cross Abstract: Federated LoRA provides a communication-efficient mechanism for fine-tuning large language models on decentralized data.

By Haoran Zhang, Dongjun Kim, Seohyeon Cha, Haris Vikalo

llms fine-tuning safety

arXiv AI

Jul 9

An Adaptive Differentially Private Federated Learning Framework

arXiv:2602. 06838v3 Announce Type: replace Abstract: Federated learning enables collaborative model training across distributed clients while preserving data privacy.

By Jin Wang, Hui Ma, Yajun Zhang, Xinjun Pei, Ming Yan, Fei Xing, Yikun Chen

safety

Related stories

PreLort: Prefix-Nested LoRA for Federated Fine-Tuning under Rank Heterogeneity

IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning

IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning

DP-Hype: Federated Differentially Private Hyperparameter Search

FedRot-LoRA: Mitigating Rotational Misalignment in Federated LoRA

An Adaptive Differentially Private Federated Learning Framework