| dc.description.abstract | etd.lib.nycu.edu.tw/cgi-bin/gs32/ncugsweb.cgi/ccd=PziWQO/login?jstimes=1&loadingjs=1&userid=guest&o=dwebmge&cache=1722348367122With the widespread adoption of smart mobile devices, developing distributed learningalgorithms and data fusion techniques with privacy protection features for low-cost edge orlocal devices has become a crucial research topic. Distributed compressive learning integratescompressive sensing techniques to significantly reduce the measurements required for sparsesignal reconstruction, and also utilize the sensing matrix as a cryptographic key for dataencryption. Such collaborative learning methods are now widely applied in the field of AIcommunications. However, existing collaborative learning methods mainly rely on thecondition that both amplitude and phase information can be simultaneously obtained from themeasurements during the sampling process. This paper is the first to address the issue ofdistributed compressive phase retrieval, specifically investigating how local devices within anetwork can collaboratively reconstruct sparse signals with only limited measurementamplitude information.Considering the potential limitations due to geographical location or sensing capabilitiesof local devices in networks, this study examines a multi-view compressive phase retrievalproblem where each device can observe only a partial view of the global sparse signal. Here,partial view means that certain arbitrary and unknown indices of the global signal vector areunobservable to that device and, therefore, do not influence the measurement outcomes. In thiswork, the objective for each local device is to reconstruct its local partial-view signal vectorusing its own measurements and messages received from the central server about the globalsignal, all while preserving the privacy of each device. In addition, recognizing the need forhigh transmission rates and low latency in next-generation network communication systems, aivsparse sensing technique is employed at each local device to reduce data gathering and storagecosts. By exploiting the sparse nature of both the signal and sensing matrices, the global signalsupport can be identified using a simple counting rule. Then based on this support estimate, thecorresponding nonzero signal entries in the global signal vector can be recovered by solving aleast squares problem. Subsequently, a distributed alternating direction method of multipliers(ADMM)-based iterative algorithm is proposed for local sparse signal reconstruction. Notably,our proposed collaborative distributed learning scheme enables joint reconstruction of acommon sparse signal by local clients without the need to share local datasets with the centralserver, while also recovering personalized sparse vectors, i.e., partial-view vectors.Consequently, the proposed recovery method incorporates notable privacy protection features.In order to reduce the computational costs and the inconvenience of manually tuning theoptimal parameters during the execution of the ADMM-based algorithm, this study furtheradopts deep unfolding techniques to propose a new model-driven deep learning architectureaimed at enhancing the energy efficiency of collaborative learning. Simulation results show thateven though the number of measurements obtained by local devices in the network is far lessthan what is needed for independent signal reconstruction, our proposed collaborativedistributed learning algorithm can still achieve stable signal reconstruction through cooperationwithout sharing raw measurement data. Furthermore, the estimation accuracy of our methodsignificantly outperforms that of many existing methods specifically designed for compressivephase retrieval. | en_US |