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: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
arXiv:2504. 17471v2 Announce Type: replace-cross Abstract: Gossip Learning (GL) is a decentralized learning paradigm where users iteratively exchange and aggregate models with a small set of neighboring peers.
By Yacine Belal, Mohamed Maouche, Sonia Ben Mokhtar
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
The paper investigates how the strategic placement of Byzantine nodes in decentralized federated learning (DFL) affects the propagation of malicious influence across the communication graph. It introduces Byzantine Placement Influence (BPI), a measure that captures cumulative exposure of honest nodes to Byzantine sources over time, and develops algorithms to optimize BPI across various network structures and attack types. Experiments demonstrate that BPI-guided placements consistently yield highly damaging configurations, highlighting the importance of considering node placement in DFL threat models.
By Edoardo Gabrielli, Gabriele Tolomei
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