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
Dealing simultaneously with confidentiality and Byzantine behaviors in decentralized learning is a challenging problem. Indeed, in decentralized learning, clients train a machine learning model while keeping their data locally and share their model parameters or gradients with a set of neighbors.
arXiv:2607. 08651v1 Announce Type: new Abstract: Decentralized federated learning (DFL) removes the central server by letting nodes exchange model updates through peer-to-peer gossip, but existing gossip-based methods often lack provenance finality and resilience to Byzantine or lazy participants.
By Amirhossein Taherpour, Xiaodong Wang
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.
By Ajay Kumar Shrestha
The paper reviews four studies that combine blockchain and AI to secure data sharing, model integrity, and autonomous decision-making in distributed systems. It highlights how blockchain’s immutability, decentralized consensus, and verifiable provenance can address trust gaps in training data, real‑time monitoring, and automated code remediation. The authors propose a layered architecture integrating hardened models, blockchain‑anchored provenance, AI anomaly detection, and smart‑contract‑governed multi‑agent remediation, and outline open challenges in scalability, privacy‑transparency trade‑offs, and governance.
By Harsh Verma
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