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This project uses Python and machine learning to classify plant species as poisonous or non-poisonous. It aims to provide an efficient way to identify safe and harmful plants, useful for botanists, hikers, and the agricultural sector.
M. Anisetti, C. A. Ardagna, A. Balestrucci, N. Bena, E. Damiani, C. Y. Yeun. "On the Robustness of Random Forest Against Data Poisoning: An Ensemble-Based Approach". In IEEE TSUSC, vol. 8 no. 4
FedAnil+ is a novel lightweight, and secure Federated Deep Learning Model to address non-IID data, privacy concerns, and communication overhead. This repo hosts a simulation for FedAnil+ written in Python.
FedAnil++ is a Privacy-Preserving and Communication-Efficient Federated Deep Learning Model to address non-IID data, privacy concerns, and communication overhead. This repo hosts a simulation for FedAnil++ written in Python.
FedAnil is a secure blockchain-enabled Federated Deep Learning Model to address non-IID data and privacy concerns. This repo hosts a simulation for FedAnil written in Python.
Paper collection of federated learning. Conferences and Journals Collection for Federated Learning from 2019 to 2021, Accepted Papers, Hot topics and good research groups. Paper summary