Efficient P2P Ensemble Learning with Linear Models on Fully Distributed Data

Details Efficient P2P Ensemble Learning with Linear Models on Fully Distributed Data Efficient P2P Ensemble Learning with Linear Models on Fully Distributed Data

2011-09-07

arxiv.org/abs/1109.1396v1

Computer Science

Learning

Scientific paper

Machine learning over fully distributed data poses an important problem in peer-to-peer (P2P) applications. In this model we have one data record at each network node, but without the possibility to move raw data due to privacy considerations. For example, user profiles, ratings, history, or sensor readings can represent this case. This problem is difficult, because there is no possibility to learn local models, yet the communication cost needs to be kept low. Here we propose gossip learning, a generic approach that is based on multiple models taking random walks over the network in parallel, while applying an online learning algorithm to improve themselves, and getting combined via ensemble learning methods. We present an instantiation of this approach for the case of classification with linear models. Our main contribution is an ensemble learning method which-through the continuous combination of the models in the network-implements a virtual weighted voting mechanism over an exponential number of models at practically no extra cost as compared to independent random walks. Our experimental analysis demonstrates the performance and robustness of the proposed approach.

Affiliated with

Also associated with

No associations

Rating

Efficient P2P Ensemble Learning with Linear Models on Fully Distributed Data does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Efficient P2P Ensemble Learning with Linear Models on Fully Distributed Data, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Efficient P2P Ensemble Learning with Linear Models on Fully Distributed Data will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-305905