个性化联邦学习因其在应对数据异质性和隐私保护方面的优势而备受关注。现有算法专注于平衡全局信息和个性化信息之间的矛盾,忽视了全局信息中的不同标签信息带来的干扰,尤其在维护单一全局头部的算法中,容易出现标签间特征冲突导致的...个性化联邦学习因其在应对数据异质性和隐私保护方面的优势而备受关注。现有算法专注于平衡全局信息和个性化信息之间的矛盾,忽视了全局信息中的不同标签信息带来的干扰,尤其在维护单一全局头部的算法中,容易出现标签间特征冲突导致的收敛困难。为此,提出一种新的算法——全局多头部联邦学习(federated learning with global multi-head,FedGMH)算法,该算法在服务器创建多个全局头部,每个头部专门处理一种标签信息,而客户端下载与本地标签相关的全局头部,从而避免无关标签信息的干扰。此外,FedGMH引入参数级聚合机制:评估头部参数重要性,并将关键参数更新为全局多头部的加权参数,以加快收敛速度并且提高准确率。在3个视觉数据集上的大量实验表明,FedGMH优于现有的先进算法。展开更多
Sensor nodes in a wireless sensor network （WSN） are typically powered by batteries, thus the energy is constrained. It is our design goal to efficiently utilize the energy of each sensor node to extend its lifetime,...Sensor nodes in a wireless sensor network （WSN） are typically powered by batteries, thus the energy is constrained. It is our design goal to efficiently utilize the energy of each sensor node to extend its lifetime, so as to prolong the lifetime of the whole WSN. In this paper, we propose a path-based data aggregation scheme （PBDAS） for grid-based wireless sensor networks. In order to extend the lifetime of a WSN, we construct a grid infrastructure by partitioning the whole sensor field into a grid of cells. Each cell has a head responsible for aggregating its own data with the data sensed by the others in the same cell and then transmitting out. In order to efficiently and rapidly transmit the data to the base station （BS）, we link each cell head to form a chain. Each cell head on the chain takes turn becoming the chain leader responsible for transmitting data to the BS. Aggregated data moves from head to head along the chain, and finally the chain leader transmits to the BS. In PBDAS, only the cell heads need to transmit data toward the BS. Therefore, the data transmissions to the BS substantially decrease. Besides, the cell heads and chain leader are designated in turn according to the energy level so that the energy depletion of nodes is evenly distributed. Simulation results show that the proposed PBDAS extends the lifetime of sensor nodes, so as to make the lifetime of the whole network longer.展开更多
Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cause of cancer mortality in the world and the 5th most commonly occurring cancer (Siegel, R. 2014). In the last few decades a growing interest fo...Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cause of cancer mortality in the world and the 5th most commonly occurring cancer (Siegel, R. 2014). In the last few decades a growing interest for the emerging data from both tumor biology and multimodality treatment in HNSCC has been developed. A huge number of new markers need to be managed with bio-informatics systems to elaborate and correlate clinical and molecular data. Data mining algorithms are a promising medical application. We used this technology to correlate blood samples with clinical outcome in 120 patients treated with chemoradiation for locally advanced HNSCC. Our results did not find a significant correlation because of the sample exiguity but they show the potential of this tool.展开更多
文摘个性化联邦学习因其在应对数据异质性和隐私保护方面的优势而备受关注。现有算法专注于平衡全局信息和个性化信息之间的矛盾,忽视了全局信息中的不同标签信息带来的干扰,尤其在维护单一全局头部的算法中,容易出现标签间特征冲突导致的收敛困难。为此,提出一种新的算法——全局多头部联邦学习(federated learning with global multi-head,FedGMH)算法,该算法在服务器创建多个全局头部,每个头部专门处理一种标签信息,而客户端下载与本地标签相关的全局头部,从而避免无关标签信息的干扰。此外,FedGMH引入参数级聚合机制:评估头部参数重要性,并将关键参数更新为全局多头部的加权参数,以加快收敛速度并且提高准确率。在3个视觉数据集上的大量实验表明,FedGMH优于现有的先进算法。
基金supported by the NSC under Grant No.NSC-101-2221-E-239-032 and NSC-102-2221-E-239-020
文摘Sensor nodes in a wireless sensor network （WSN） are typically powered by batteries, thus the energy is constrained. It is our design goal to efficiently utilize the energy of each sensor node to extend its lifetime, so as to prolong the lifetime of the whole WSN. In this paper, we propose a path-based data aggregation scheme （PBDAS） for grid-based wireless sensor networks. In order to extend the lifetime of a WSN, we construct a grid infrastructure by partitioning the whole sensor field into a grid of cells. Each cell has a head responsible for aggregating its own data with the data sensed by the others in the same cell and then transmitting out. In order to efficiently and rapidly transmit the data to the base station （BS）, we link each cell head to form a chain. Each cell head on the chain takes turn becoming the chain leader responsible for transmitting data to the BS. Aggregated data moves from head to head along the chain, and finally the chain leader transmits to the BS. In PBDAS, only the cell heads need to transmit data toward the BS. Therefore, the data transmissions to the BS substantially decrease. Besides, the cell heads and chain leader are designated in turn according to the energy level so that the energy depletion of nodes is evenly distributed. Simulation results show that the proposed PBDAS extends the lifetime of sensor nodes, so as to make the lifetime of the whole network longer.
文摘Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cause of cancer mortality in the world and the 5th most commonly occurring cancer (Siegel, R. 2014). In the last few decades a growing interest for the emerging data from both tumor biology and multimodality treatment in HNSCC has been developed. A huge number of new markers need to be managed with bio-informatics systems to elaborate and correlate clinical and molecular data. Data mining algorithms are a promising medical application. We used this technology to correlate blood samples with clinical outcome in 120 patients treated with chemoradiation for locally advanced HNSCC. Our results did not find a significant correlation because of the sample exiguity but they show the potential of this tool.
