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Erschienen in:
2016 | OriginalPaper | Buchkapitel
Optimization of Outsourcing ELM Problems in Cloud Computing from Multi-parties
verfasst von : Jiarun Lin, Tianhang Liu, Zhiping Cai, Xinwang Liu, Jianping Yin
Erschienen in: Proceedings of ELM-2015 Volume 2
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Abstract
In this letter, we introduce a secure and practical multi-parties cooperating mechanism of outsourcing extreme learning machines (ELM) in Cloud Computing. This outsourcing mechanism enables original ELM to perform over large-scale dataset in which multi-parties are involved. We propose a optimized partition policy in Cloud Computing to significantly improve the training speed and dramatically reduce the communication overhead. According to the partition policy, cloud servers are mainly responsible for calculating the inverse of an intermediate matrix derived from the hidden layer output matrix, which is the heaviest computation. Although most of the computation is outsourced in Cloud Computing, the confidentiality of the input/output is assured because the randomness of the hidden layer is fully exploited. Theoretical analysis and experiments have shown that the proposed multi-parties cooperating mechanism for outsourcing ELM can effectively release customers from heavy computations.