arXiv AI July 15, 2026 By Jing Liu, Kun Yang, Yan Wang, Dingkang Yang, Xiaoshuai Hao, Wei Zhang, Yang Liu, Wei Zhou

PFAdapter: Hierarchical LoRA Decomposition for Personalized Federated MLLMs

arXiv:2607. 12111v1 Announce Type: cross Abstract: Agentic AI systems are reshaping communications and networking by deploying autonomous intelligent agents capable of collaborative learning while maintaining data privacy at network edges.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Jul 30

FedWeave: Rethinking the Unit of Specialization in Heterogeneous Federated MoE-LoRA

arXiv:2607. 26618v1 Announce Type: new Abstract: Federated PEFT enables LLMs to collaboratively adapt to decentralized private data without sharing raw examples.

By Donghang Duan, Xu Zheng, Lizong Zhang, Chong Mu, Meng Han

llms fine-tuning benchmarks

Hugging Face Trending Papers

Jul 29

FedWeave: Rethinking the Unit of Specialization in Heterogeneous Federated MoE-LoRA

Federated PEFT enables LLMs to collaboratively adapt to decentralized private data without sharing raw examples. However, task heterogeneity across clients can cause cross-task interference and gradient conflicts during aggregation.

llms fine-tuning benchmarks

arXiv AI

Sep 15

Task-Aware Federated Fine-Tuning for MoE-based Large Language Models

The paper introduces FedTAR, a task-aware federated fine‑tuning approach for Mixture‑of‑Experts (MoE) large language models. FedTAR links local client updates to task preferences using routing outputs and Singular Value Decomposition to extract low‑dimensional task coordinates and update directions. It then aggregates updates within and across task clusters, reconstructing the final update to preserve expert specialization and reduce interference, achieving state‑of‑the‑art performance on four benchmark tasks under non‑IID settings.

By Tingqi Wang, Hongyu Ke, Haoxin Wang, Rafal Angryk, Zhipeng Cai

llms fine-tuning benchmarks

arXiv AI

Jun 11

Resource-Aware LLM Reasoning for Mobile Edge General Intelligence

arXiv:2509. 23248v3 Announce Type: replace Abstract: The rapid advancement of large language models (LLMs) has enabled an emergence of agentic artificial intelligence (AI) with powerful reasoning and autonomous decision-making capabilities.

By Mingyi Luo, Ruichen Zhang, Xiangwang Hou, Jun Du, Chunxiao Jiang, Yong Ren, Shiwen Mao

llms agents safety

arXiv AI

Jul 15

Continual Learning with Elastic Regularization and Synthetic Replay for Federated MLLM Fine-Tuning

arXiv:2607. 12112v1 Announce Type: cross Abstract: Federated fine-tuning of Multimodal Large Language Models (MLLMs) across distributed networks enables privacy-sensitive adaptation to evolving data streams, yet a fundamental obstacle prevents robust deployment in dynamic environments: catastrophic forgetting, wherein sequential task updates erase previously acquired knowledge across visual, linguistic, and cross-modal representations.

By Jing Liu, Chenxuanyin Zou, Jiayang Ren, Gaoyun Fang, Chengfang Li, Yan Wang, Zhenchao Ma, Bo Hu

llms rag agents fine-tuning multimodal benchmarks safety

arXiv AI

Sep 15

CIDERS: Cloud-Edge LLM Collaborative Learning via Accelerating Personalized Bilevel Optimization

arXiv:2609.15664v1 Announce Type: cross Abstract: Amid the rapid advancement of physical-world intelligence, cloud-edge collaborative large language models (LLMs) have emerged as a promising roadmap...

By Victor H. Chen, Hairui Yu, Stella K. Chung, Hong Yan

llms rag efficiency

One email a morning, machine-written

Related stories

FedWeave: Rethinking the Unit of Specialization in Heterogeneous Federated MoE-LoRA

FedWeave: Rethinking the Unit of Specialization in Heterogeneous Federated MoE-LoRA

Task-Aware Federated Fine-Tuning for MoE-based Large Language Models

Resource-Aware LLM Reasoning for Mobile Edge General Intelligence

Continual Learning with Elastic Regularization and Synthetic Replay for Federated MLLM Fine-Tuning

CIDERS: Cloud-Edge LLM Collaborative Learning via Accelerating Personalized Bilevel Optimization