arXiv Machine Learning July 2, 2026 By Zhanting Zhou, Zeyu Ma, Ziqiang Zheng, Yang Yang

FedIA: Importance-Aware Aggregation for Domain-Robust Federated Graph Learning

Read the original on arXiv Machine Learning →

arXiv:2509. 18171v4 Announce Type: replace Abstract: Federated graph learning (FGL) is a natural paradigm for social-media user graphs, where language communities, regional markets, and service boundaries can prevent raw graph pooling.

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benchmarks

arXiv Machine Learning

Jul 31

Dissecting Federated-Graph Aggregation under Domain Shift: Importance-Aware Aggregation via Empirical Analysis

arXiv:2509. 18171v5 Announce Type: replace Abstract: Federated graph learning (FGL) trains a shared graph model across clients whose local graphs differ in node features, labels, and connectivity while keeping raw graph data decentralized.

By Zhanting Zhou, Kahou Tam, Zeyu Ma, Ziqiang Zheng

arXiv Machine Learning

Aug 3

MMFGU: Multimodal Federated Graph Unlearning

arXiv:2607. 28708v1 Announce Type: new Abstract: Multimodal federated graph learning enables clients to collaboratively train graph models over structural, textual, and visual signals without sharing private local data.

By Haodong Lu, Zekai Chen, Weiwei Ji, Shihao Li, Xunkai Li, Xun Wu, Yinlin Zhu, Rong-Hua Li

multimodal

arXiv AI

Sep 10

FedRAW: Preserving Rare-Label Influence in Asynchronous Federated Learning

arXiv:2609.07192v1 Announce Type: cross Abstract: Asynchronous federated learning improves scalability by updating the global model from a server-side buffer of client updates as they arrive, rather...

By Prashant Bajpai, Divya Saxena, Philippe Lalanda, German Vega

safety

Hugging Face Trending Papers

Sep 7

FedRAW: Preserving Rare-Label Influence in Asynchronous Federated Learning

Asynchronous federated learning improves scalability by updating the global model from a server-side buffer of client updates as they arrive, rather than waiting for all selected clients to finish. Wh...

safety

arXiv AI

Jun 6

Amortizing Federated Adaptation: Hypernetwork Driven LoRA for Personalized Foundation Models

arXiv:2606. 06154v1 Announce Type: new Abstract: Federated fine-tuning of foundation models using Low-Rank Adaptation (LoRA) offers a communication efficient solution for distributed learning.

By Sunny Gupta, Shambhavi Shanker, Amit Sethi

fine-tuning multimodal benchmarks safety

arXiv Machine Learning

Sep 7

PACE: Propagation-Aware Collaborative Correction for One-Shot Personalized Federated Graph Learning

PACE introduces a propagation‑aware collaborative correction for one‑shot personalized federated graph learning. Each client sends a rank‑r update and a diagonal sketch of message moments, allowing the server to construct a correction that anchors to the receiver’s local model. A convex negative‑log‑likelihood calibration selects a single coefficient to blend local and external logits, improving accuracy and weighted‑F1 on most datasets while preserving local predictions when the correction is unhelpful.

By Ruizhe Huang, Chengran Li, Xiaochuan Shi

One email a morning, machine-written

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Dissecting Federated-Graph Aggregation under Domain Shift: Importance-Aware Aggregation via Empirical Analysis

MMFGU: Multimodal Federated Graph Unlearning

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