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PRoVeFL: Private Robust and Verifiable Aggregation in Federated Learning

BACKGROUND
1 SIGNALFIRST DETECTED 9 July 2026
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9 Jul
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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

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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

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24 Aug
FL-MAESTRO: Multi-Agent LLM Orchestration for Resource-Constrained Federated Learning

arXiv:2608.20518v1 Announce Type: new Abstract: In Federated Learning (FL), the communication topology is a runtime variable rather than a fixed design choice, since links and

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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

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20 Mar
Probabilistic Federated Learning on Uncertain and Heterogeneous Data with Model Personalization

arXiv:2603.18083v1 Announce Type: cross Abstract: Conventional federated learning (FL) frameworks often suffer from training degradation due to data uncertainty and heterogeneity

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30 Apr
Privacy-Preserving Federated Learning Framework for Distributed Chemical Process Optimization

arXiv:2604.26073v1 Announce Type: cross Abstract: Industrial chemical plants often operate under strict data confidentiality constraints, making centralized data-driven process

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20 Mar
FedTrident: Resilient Road Condition Classification Against Poisoning Attacks in Federated Learning

arXiv:2603.19101v1 Announce Type: cross Abstract: FL has emerged as a transformative paradigm for ITS, notably camera-based Road Condition Classification (RCC). However, by

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28 Apr
Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy

arXiv:2604.22562v1 Announce Type: cross Abstract: Client contribution estimation in Federated Learning is necessary for identifying clients' importance and for providing fair

REPORT · HOW · ArXiv CS.AIOPEN STORY
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