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Distributed Machine Learning (DML) is one of the core technologies for Artificial Intelligence (AI). However, in the existing distributed machine learning framework, the data integrity is not taken into account. If network attackers forge the data, modify the data, or destroy the data, the training model in the distributed machine learning system will be greatly affected, and the training results are led to be wrong. Therefore, it is crucial to guarantee the data integrity in the DML. In this paper, we propose a distributed machine learning oriented data integrity verification scheme (DML-DIV) to ensure the integrity of training data. Firstly, we adopt the idea of Provable Data Possession (PDP) sampling auditing algorithm to achieve data integrity verification so that our DML-DIV scheme can resist forgery attacks and tampering attacks. Secondly, we generate a random number, namely blinding factor, and apply the discrete logarithm problem (DLP) to construct proof and ensure privacy protection in the TPA verification process. Thirdly, we employ identity-based cryptography and two-step key generation technology to generate data owner's public/private key pair so that our DML-DIV scheme can solve the key escrow problem and reduce the cost of managing the certificates. Finally, formal theoretical analysis and experimental results show the security and efficiency of our DML-DIV scheme. INDEX TERMS Distributed machine learning, public auditing, data integrity, bilinear mapping, identity-based cryptography.

Underwater wireless optical communication (UWOC) will play an important role in the underwater environment exploration and marine resource development due to its advantages of high data rate and good mobility. However, the significant signal power attenuation in the underwater channel limits the transmission distance of UWOC. Attenuation length (AL) is widely used as an indicator for evaluating the UWOC system's long-distance transmission capability. At present, Gbps UWOC is limited within 7AL. Using a SiPM based receiver can dramatically increase the AL that UWOC can support. In this paper, a novel UWOC receiver built from an off-the-shelf SiPM has been demonstrated. The finite pulse width and limited bandwidth of SiPM limit the SiPM based UWOC system's data rate. To boost the system's data rate, an optimum method to process the SiPM's signal has therefore been investigated. Based on these methods, the communication capabilities of the SiPM based UWOC have been investigated experimentally. Results show that the SiPM based receiver can support 11.6AL without turbulence and 9.28AL within weak turbulence (scintillation index = 0.0447) at 1 Gbps.

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Using the Debye model and existing experimental data of the pyroelectric coefficient of AlN, the temperature dependence of the pyroelectric coefficient as well as the spontaneous polarization of AlN is calculated over a wide temperature range from 0to1000K. The pyroelectric coefficient is proportional to T3 at low temperature and increases acutely from 0 to around 400K, and then increases gently from 400to1000K. It makes AlN uniquely suitable for application in high temperature pyroelectric sensors. The spontaneous polarization of AlN changes a little from 0to1000K, which indicates that the features of III-nitrides based devices will hardly be degraded by the change of the spontaneous polarization.