Skip to content
Log in

Similar papers

#federated learning Open access Oct 2026

SoK: Verifiable Integrity Claims for Privacy-Preserving Federated Learning

This SoK model federated learning as an append-only transcript of submissions, admissions, aggregation, and finalization events, and formalize verifiability as a collection of integrity claims issued by clients and the aggregator, and checked by different verifier classes.

Andrea Rizzini, Marco Esposito, Tommaso Gagliardoni et al. · 0 citations

Auditability for Secure Machine Learning in the Cloud

VeriProv is proposed, a provenance-based framework for enforcing and auditing non-repudiable and verifiable ML workflows in adversarial cloud environments.

D. Ferreira, Luís E. T. Rodrigues, Nuno Santos · 0 citations
Preprint Aug 2026

Manipulation-Proof Oblivious Audits against Deceptive Model Providers

A novel audit protocol designed to significantly increase the post-audit detectability of manipulations by enabling the auditor to query the model in an oblivious manner and providing theoretical guarantees showing that, under this protocol, a provider attempting to hide unfairness must falsify a significantly larger number of responses.

Augustin Godinot, Sofiane Azogagh, Julien Ferry et al. · 0 citations
Open access Sep 2025

Poison to Detect: Detection of Targeted Overfitting in Federated Learning

An underexplored threat in which a dishonest orchestrator intentionally manipulates the aggregation process to induce targeted overfitting in local models of specific clients is studied, demonstrating that client-side integrity testing can provide early, effective, and scalable detection, supporting safer deployment of FL systems.

Soumia Zohra El Mestari, M. Zuziak, Gabriele Lenzini · 0 citations
Review Open access Aug 2026

Poisoning Attacks in Federated Learning: An Accountability- Oriented Survey with Centralized Learning as a Baseline

Artificial intelligence (AI) systems are increasingly deployed in high-stakes domains, where poisoning attacks can corrupt training data, manipulate model updates, or implant covert backdoors. This survey examines poisoning attacks in federated learning (FL), using centralized learning as a baseline to explain how distributed data, client heterogeneity, privacy-preserving aggregation, and untrusted coordination expand the threat surface. It positions prior surveys and synthesizes representative primary studies through an accountability-oriented lens focused on attribution, audit evidence, traceability, and forensic readiness. The review compares major attack classes, including data poisoning, model poisoning, backdoor insertion, server-side manipulation, Sybil behavior, collusion, and multi-round poisoning. It also evaluates countermeasures such as Byzantine-robust aggregation, anomaly detection, validation-based filtering, malicious-secure aggregation, authenticated update handling, provenance mechanisms, ledger-based evidence, and verifiable aggregation protocols. The analysis shows that robustness alone is insufficient for trustworthy FL unless defenses also preserve evidence that supports independent verification, post-incident reconstruction, and governance review. Persistent gaps remain in causal forensic attribution, privacy-preserving evidence governance, malicious-server threat modeling, scalable verifiability tooling, recovery after poisoning, and deployment-ready benchmarks. The survey concludes that accountable FL should be designed as an evidence-producing system, not merely as a privacy-preserving or attack-resistant training architecture, especially for regulated, cross-silo, and high-risk real-world deployments.

S. Mohammed, D. Alhadidi, A. Ngom · 0 citations