Decentralized Federated Learning with Multimodal Prototypes for Heterogeneous Data
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- Year
- 2026
- Venue
- Information Fusion
- Identifier
- martinezbeltran2026decentralized
Suggested citation
Enrique Tomás Martínez Beltrán, Gérôme Bovet, Gregorio Martínez Pérez, Alberto Huertas Celdrán (2026). Decentralized Federated Learning with Multimodal Prototypes for Heterogeneous Data. Information Fusion.
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Asynchronous Cache-based Aggregation with Fairness and Filtering for Decentralized Federated Learning
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Decentralized Federated Learning (DFL) offers a scalable paradigm for collaborative intelligence at the edge, yet its practical efficacy is severely constrained by system heterogeneity. Traditional synchronous protocols enforce rigid, locks...
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Traffic accidents are the leading cause of death among young people, a problem that today costs an enormous number of victims. Several technologies have been proposed to prevent accidents, being brain--computer interfaces (BCIs) one of the...
Analyzing the robustness of decentralized horizontal and vertical federated learning architectures in a non-IID scenario
Pedro Miguel Sánchez Sánchez, Alberto Huertas Celdrán, Enrique Tomás Martínez Beltrán, Daniel Demeter, Gérôme Bovet, Gregorio Martínez Pérez, Burkhard Stiller
Federated learning (FL) enables participants to collaboratively train machine and deep learning models while safeguarding data privacy. However, the FL paradigm still has drawbacks that affect its trustworthiness, as malicious participants...
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