arXiv AI August 7, 2026 By Ajay Kumar Shrestha

Trust-Based Incentive Mechanisms in Semi-Decentralized Federated Learning Systems

arXiv:2602. 08290v2 Announce Type: replace-cross Abstract: In federated learning (FL), decentralized model training allows multi-ple participants to collaboratively improve a shared machine learning model without exchanging raw data.

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.

data-engineering

arXiv AI

Jul 8

Trust-free Personalized Decentralized Learning

arXiv:2410. 11378v3 Announce Type: replace-cross Abstract: Personalized collaborative learning in federated settings faces a critical trade-off between customization and participant trust.

By Yawen Li, Yan Li, Junping Du, Yingxia Shao, Meiyu Liang, Guanhua Ye

efficiency safety

arXiv Machine Learning

Aug 11

Robust Reputation-Driven Crowdsourced Federated Learning

arXiv:2608. 08574v1 Announce Type: new Abstract: Crowdsourced Federated Learning (CrowdFL) extends traditional federated learning by enabling open and heterogeneous participation through a crowdsourcing paradigm.

By Mouhamed Amine Bouchiha, Gregory Blanc

benchmarks

arXiv Machine Learning

Jun 18

Automated Byzantine-Resilient Clustered Decentralized Federated Learning for Battery Intelligence in Connected EVs

arXiv:2605. 21115v2 Announce Type: replace-cross Abstract: Federated learning (FL) has emerged as a promising paradigm for managing electric vehicle (EV) battery data in intelligent transportation systems (ITS), enabling privacy-preserving tasks such as anomaly detection and capacity estimation.

By Mouhamed Amine Bouchiha, Abdelaziz Amara Korba, Yacine Ghamri-Doudane

benchmarks safety

arXiv Machine Learning

Aug 24

BackDFL: A Unified Benchmark For Backdoor Attacks and Defenses In Decentralized Federated Learning

arXiv:2608.21137v1 Announce Type: new Abstract: Decentralized Federated Learning (DFL) promises trust-free collaborative learning by replacing the centralized parameter server with peer-to-peer model...

By Mouhamed Amine Bouchiha, Gregory Blanc, Yufei Han

benchmarks

arXiv Machine Learning

Jun 11

SEDULity: A Proof-of-Learning Framework for Distributed and Secure Blockchains with Efficient Useful Work

arXiv:2512. 13666v2 Announce Type: replace-cross Abstract: The security and decentralization of Proof-of-Work (PoW) have been well-tested in existing blockchain systems.

By Weihang Cao, Mustafa Doger, Sennur Ulukus

arXiv Machine Learning

Sep 15

End-to-End Verifiable and Robust Federated Learning

arXiv:2609.15521v1 Announce Type: new Abstract: Federated learning enables multiple parties to train a shared model without centralizing raw data with the help of an aggregator, but introduces integr...

By Doryan Lesaignoux, Enrique M\'armol Campos, Gabriele Spini, Jos\'e L. Hern\'andez-Ramos, Stephan Krenn

One email a morning, machine-written

Related stories

Trust-free Personalized Decentralized Learning

Robust Reputation-Driven Crowdsourced Federated Learning

Automated Byzantine-Resilient Clustered Decentralized Federated Learning for Battery Intelligence in Connected EVs

BackDFL: A Unified Benchmark For Backdoor Attacks and Defenses In Decentralized Federated Learning

SEDULity: A Proof-of-Learning Framework for Distributed and Secure Blockchains with Efficient Useful Work

End-to-End Verifiable and Robust Federated Learning