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• W4294811180 abstract "Federated learning (FL) is an emerging technique to support privacy-aware and resource-constrained machine learning, where a base station (BS) will coordinate a set of distributed Internet of Things (IoT) devices to train a shared machine learning model with their local data sets. Nevertheless, due to the frequent interactions between BS and distributed IoT devices for the aggregating/distributing learning model parameters, the performance of FL is fundamentally restricted by the randomness of channel condition. To address this issue, we utilize the intelligent reflecting surface (IRS) to improve the efficiency of learning model aggregation/distribution. In addition, we consider two transmission protocols to enable the model aggregation from IoT devices to BS, i.e., frequency division multiple access (FDMA) and nonorthogonal multiple access (NOMA). For both protocols, we formulate the total training latency minimization problem under the available energy constraints of IoT devices, to jointly optimize the phase shifts of IRS, communication resource scheduling, and transmit power and local computing frequencies of IoT devices. Moreover, we further develop the efficient multidimensional resource management algorithms to solve the formulated training latency minimization problems. Numerical results demonstrate that the proposed IRS-assisted FL systems can achieve significant latency reduction as compared with other benchmark methods, and the NOMA-based model aggregation method exhibits a lower total training latency than the FDMA-based counterpart." @default.
• W4294811180 created "2022-09-06" @default.
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• W4294811180 date "2023-01-15" @default.
• W4294811180 modified "2023-10-18" @default.
• W4294811180 title "Intelligent Reflecting Surface-Assisted Low-Latency Federated Learning Over Wireless Networks" @default.
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• W4294811180 doi "https://doi.org/10.1109/jiot.2022.3204637" @default.
• W4294811180 hasPublicationYear "2023" @default.
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