Hugging Face Trending Papers July 7, 2026

PRoVeFL: Private Robust and Verifiable Aggregation in Federated Learning

Read the original on Hugging Face Trending Papers →

Federated Learning (FL) enables multiple clients to collaboratively train machine learning models while retaining data locality, thereby enhancing user privacy. However, traditional FL frameworks rely on a centralized aggregation server and assume honest-but-curious clients, making them susceptible to both server-side inference and client-side poisoning attacks.

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 Hugging Face Trending Papers.

arXiv AI

Jul 9

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, thereby enhancing user privacy.

By Harsh Kasyap, Anil Kumar Pradhan, Ugur Ilker Atmaca, Graham Cormode, Carsten Maple

benchmarks safety

arXiv Machine Learning

Jun 9

Scalable and Private Federated Learning Using Distributed Differential Privacy and Secure Aggregation

arXiv:2604. 07125v2 Announce Type: replace-cross Abstract: This article presents DDP-SA, a scalable privacy-preserving federated learning framework that jointly leverages client-side local differential privacy (LDP) and full-threshold additive secret sharing (ASS) for secure aggregation.

By Wenjing Wei, Farid Nait-Abdesselam, Alla Jammine

safety

arXiv Machine Learning

Sep 3

Pushing Forward Multi-Secret-Key Homomorphic Encryption for Private Average Aggregation

arXiv:2609. 01945v1 Announce Type: cross Abstract: Federated Learning enables multiple clients to train a shared model while keeping their local datasets isolated.

By Miguel Morona-M\'inguez, Fernando P\'erez-Gonz\'alez, Alberto Pedrouzo-Ulloa

benchmarks

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

arXiv Machine Learning

Jul 31

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata

arXiv:2607. 28338v1 Announce Type: new Abstract: Clustered Federated Learning (CFL) addresses data heterogeneity in federated settings by grouping clients with similar data distributions to enable effective training.

By Michael Ben Ali, Imen Megdiche, Andr\'e P\'eninou, Olivier Teste

safety

arXiv Machine Learning

Jun 18

Giskard : Byzantine Robust and Confidential Aggregation for Large-Scale Decentralized Learning

arXiv:2606. 19129v1 Announce Type: cross Abstract: Dealing simultaneously with confidentiality and Byzantine behaviors in decentralized learning is a challenging problem.

By Ousmane Touat, C\'esar Sabater, Mohamed Maouche, Sonia Ben Mokhtar

One email a morning, machine-written

Related stories

PRoVeFL: Private Robust and Verifiable Aggregation in Federated Learning

Scalable and Private Federated Learning Using Distributed Differential Privacy and Secure Aggregation

Pushing Forward Multi-Secret-Key Homomorphic Encryption for Private Average Aggregation

End-to-End Verifiable and Robust Federated Learning

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata

Giskard : Byzantine Robust and Confidential Aggregation for Large-Scale Decentralized Learning