NewsHive
CONTACT USANALYST PORTAL →
aiBACKGROUND

Data-Free Contribution Estimation in Federated Learning using Gradient von Neuma

BACKGROUND
1 SIGNALFIRST DETECTED 28 April 2026
Narrative synthesis pending — 1 signal collected.
How the story developed
28 Apr
5.6
Other feeds and industry voices
9 Jul
WHERE to Generate Matters: Budget-Aware Synthetic Augmentation for Label Skewed Federated Learning

arXiv:2607.06616v1 Announce Type: cross Abstract: Label skew in federated learning (FL) causes client drift and degrades global accuracy. Synthetic data augmentation can reduce

REPORT · WHERE · ArXiv CS.AIOPEN STORY
27 Mar
Agentic Trust Coordination for Federated Learning through Adaptive Thresholding and Autonomous Decision Making in Sustainable and Resilient Industrial Networks

arXiv:2603.25334v1 Announce Type: new Abstract: Distributed intelligence in industrial networks increasingly integrates sensing, communication, and computation across

REPORT · WHAT · ArXiv CS.AIOPEN STORY
9 Jul
PRoVeFL: Private Robust and Verifiable Aggregation in Federated Learning

arXiv:2607.06612v1 Announce Type: cross Abstract: Federated Learning (FL) enables multiple clients to collaboratively train machine learning models while retaining data locality,

REPORT · WHAT · ArXiv CS.AIOPEN STORY
20 Mar
OPUS-VFL: Incentivizing Optimal Privacy-Utility Tradeoffs in Vertical Federated Learning

arXiv:2504.15995v2 Announce Type: replace-cross Abstract: Vertical Federated Learning (VFL) enables organizations with disjoint feature spaces but shared user bases to

REPORT · WHAT · ArXiv CS.AIOPEN STORY
2 Sept
A Stable Aggregation Method for Quantum Federated Learning

arXiv:2609.00356v1 Announce Type: new Abstract: Quantum federated learning (QFL) enables clients to train quantum neural network (QNN) models without sharing private data. We find

REPORT · WHERE · ArXiv CS.AIOPEN STORY
3 Aug
Evaluating Federated Pre-Training: On the Reliability of Downstream Fine-Tuning and Intrinsic Evaluation

arXiv:2607.28658v1 Announce Type: cross Abstract: Federated pre-training offers a way to train foundation models on private or distributed data without centralizing the underlying

REPORT · WHAT · ArXiv CS.AIOPEN STORY
24 Jun
Towards Federated Long-Tailed Graph Learning: An Energy-Guided Dual Decoupling Approach

arXiv:2606.24237v1 Announce Type: new Abstract: Federated Graph Learning facilitates collaborative graph modeling across distributed clients while preserving data privacy.

REPORT · WHAT · ArXiv CS.AIOPEN STORY
Sources
ArXiv CS.AI

NewsHive monitors these sources continuously. All signal titles above link to the original reporting.

Intelligence by NewsHive. Need help navigating what this means for your business? Contact GeekyBee →