The recent interest in federated learning has initiated the investigation for efficient models deployable in scenarios with strict communication and computational constraints. Furthermore, the inherent privacy concerns in decentralized and federated learning call for efficient distribution of information in a network of interconnected agents. Therefore, we propose a novel distributed classification solution that is based on shallow randomized networks equipped with a compression mechanism that is used for sharing the local model in the federated context. We make extensive use of hyperdimensional computing both in the local network model and in the compressed communication protocol, which is enabled by the binding and the superposition operations. Accuracy, precision, and stability of our proposed approach are demonstrated on a collection of datasets with several network topologies and for different data partitioning schemes.
Few-shot Federated Learning in Randomized Neural Networks via Hyperdimensional Computing / Rosato, A.; Panella, M.; Osipov, E.; Kleyko, D.. - 2022-:(2022), pp. 1-8. (Intervento presentato al convegno 2022 International Joint Conference on Neural Networks, IJCNN 2022 tenutosi a Padova, Italy) [10.1109/IJCNN55064.2022.9892007].
Few-shot Federated Learning in Randomized Neural Networks via Hyperdimensional Computing
Rosato A.
;Panella M.;
2022
Abstract
The recent interest in federated learning has initiated the investigation for efficient models deployable in scenarios with strict communication and computational constraints. Furthermore, the inherent privacy concerns in decentralized and federated learning call for efficient distribution of information in a network of interconnected agents. Therefore, we propose a novel distributed classification solution that is based on shallow randomized networks equipped with a compression mechanism that is used for sharing the local model in the federated context. We make extensive use of hyperdimensional computing both in the local network model and in the compressed communication protocol, which is enabled by the binding and the superposition operations. Accuracy, precision, and stability of our proposed approach are demonstrated on a collection of datasets with several network topologies and for different data partitioning schemes.File | Dimensione | Formato | |
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