arXiv AI August 3, 2026 By Shengkun Zhu, Jinshan Zeng, Zhihua Allen-Zhao, Mayi Xu, Quanqing Xu, Wei Ren, Qiang Yang, Yang Liu

Federated Foundation Models Fine-Tuning with Heterogeneous Compressed Clients

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arXiv:2607. 29071v1 Announce Type: cross Abstract: Federated learning of foundation models faces a fundamental resource-asymmetry challenge: the institutions holding the most valuable domain-specific data cannot host billion-parameter models.

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

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1d ago

UniFed-VLM: Federated Instruction Tuning for Vision-Language Models with Multiple Heterogeneity

arXiv:2608. 15516v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have demonstrated strong performance in multimodal understanding and generation.

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When Is Shallow Enough? Adaptive Split Federated Learning with Client-Specific Sufficiency Estimation

arXiv:2608. 15639v1 Announce Type: cross Abstract: \textit{Split Federated Learning} (SFL) enables distributed model training by splitting networks between the server and clients.

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benchmarks safety

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Rethinking Federated Graph Foundation Models: A Graph-Language Alignment-based Approach

arXiv:2601. 21369v2 Announce Type: replace Abstract: Recent studies of federated graph foundational models (FedGFMs) break the idealized and untenable assumption of having centralized data storage to train graph foundation models, and accommodate the reality of distributed, privacy-restricted data silos.

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llms rag fine-tuning efficiency safety

arXiv AI

Jul 10

FedOPAL: One-Shot Federated Learning via Analytic Visual Prompt Tuning

arXiv:2607. 08368v1 Announce Type: new Abstract: With the widespread deployment of basic models in edge intelligence, communication bandwidth has become a core bottleneck restricting the scalability of federated learning.

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arXiv Machine Learning

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Personalized Additive Modeling for Multi-level Federated Learning

arXiv:2405. 16472v2 Announce Type: replace Abstract: Contemporary AI faces the challenge of balancing generality with user-specific personalization.

By Shutong Chen, Guodong Long, Tianyi Zhou, Jie Ma, Jing Jiang, Chengqi Zhang

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

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