Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  studies coordination and consensus mechanisms for Wireless sensor networks in order to train a Support Vector Machine (SVM) classifier in a distributed fashion.  propose two selective gossip algorithms, which take advantage of the sparse representation that SVMs provide for their decision boundary (hyperplane), in order to ensure convergence to an optimal or close-to-optimal classifier, while minimizing the required amount of information exchange beten neighboring sensors. The first proposed algorithm calls for the local exchange of support vectors beten sensors, while the second technique requires the exchange of all sample vectors that define uniquely and completely the convex hulls of the two classes. Through simulation experiments,  show that the proposed algorithms achieve a consensus close to the desired hyperplane obtained with a centralized SVM-based classifier that uses the entire sensor data.
['K. Flouri', 'Baltasar Beferull-Lozano', 'Panagiotis Tsakalides']
I am writing a research paper on a idea Distributed consensus algorithms for SVM training in wireless sensor networks
[{"from": "human", "value": "I am writing a research paper on a idea Distributed consensus algorithms for SVM training in wireless sensor networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  studies coordination and consensus mechanisms for Wireless sensor networks in order to train a Support Vector Machine (SVM) classifier in a distributed fashion.  propose two selective gossip algorithms, which take advantage of the sparse representation that SVMs provide for their decision boundary (hyperplane), in order to ensure convergence to an optimal or close-to-optimal classifier, while minimizing the required amount of information exchange beten neighboring sensors. The first proposed algorithm calls for the local exchange of support vectors beten sensors, while the second technique requires the exchange of all sample vectors that define uniquely and completely the convex hulls of the two classes. Through simulation experiments,  show that the proposed algorithms achieve a consensus close to the desired hyperplane obtained with a centralized SVM-based classifier that uses the entire sensor data."}]