Federated learning effectively addresses issues such as data privacy by collaborating across participating devices to train global models.However,factors such as network topology and computing power of devices can aff...Federated learning effectively addresses issues such as data privacy by collaborating across participating devices to train global models.However,factors such as network topology and computing power of devices can affect its training or communication process in complex network environments.Computing and network convergence(CNC)of sixth-generation(6G)networks,a new network architecture and paradigm with computing-measurable,perceptible,distributable,dispatchable,and manageable capabilities,can effectively support federated learning training and improve its communication efficiency.By guiding the participating devices'training in federated learning based on business requirements,resource load,network conditions,and computing power of devices,CNC can reach this goal.In this paper,to improve the communication eficiency of federated learning in complex networks,we study the communication eficiency optimization methods of federated learning for CNC of 6G networks that give decisions on the training process for different network conditions and computing power of participating devices.The simulations address two architectures that exist for devices in federated learning and arrange devices to participate in training based on arithmetic power while achieving optimization of communication efficiency in the process of transferring model parameters.The results show that the methods we proposed can cope well with complex network situations,effectively balance the delay distribution of participating devices for local training,improve the communication eficiency during the transfer of model parameters,and improve the resource utilization in the network.展开更多
An inclusion complex of podophyllotoxin (PPT) with 7-cyclodextrin (7-CD) was prepared. The behavior, char- acterization, and water solubility of the inclusion complex were carefully investigated via fluorescence s...An inclusion complex of podophyllotoxin (PPT) with 7-cyclodextrin (7-CD) was prepared. The behavior, char- acterization, and water solubility of the inclusion complex were carefully investigated via fluorescence spectroscopy, thermogravimetry, differential scanning calorimetry, X-ray diffraction analysis, and 1H and 2D nuclear magnetic resonance spectroscopy. Furthermore, antitumor activity to human cancer lines and toxicity in mice were studied. Results showed that the inclusion complex formed in a 1 : 1 ratio with a considerable apparent stability constant Ks (4245.5 Lomol-l). Water solubility was considerably improved. In addition, the anticancer activity of the inclusion complex was better than that of cis-platinum (DDP, positive control). Most importantly, the toxicity of podophyl- lotoxin inclusion complex reduced and became more safety to mice which will be great valuable to research its ap- plications as a kind of antitumor drug to human in the further.展开更多
基金supported by the National Natural Science Foundation of China(Nos.62271062 and 62071063)。
文摘Federated learning effectively addresses issues such as data privacy by collaborating across participating devices to train global models.However,factors such as network topology and computing power of devices can affect its training or communication process in complex network environments.Computing and network convergence(CNC)of sixth-generation(6G)networks,a new network architecture and paradigm with computing-measurable,perceptible,distributable,dispatchable,and manageable capabilities,can effectively support federated learning training and improve its communication efficiency.By guiding the participating devices'training in federated learning based on business requirements,resource load,network conditions,and computing power of devices,CNC can reach this goal.In this paper,to improve the communication eficiency of federated learning in complex networks,we study the communication eficiency optimization methods of federated learning for CNC of 6G networks that give decisions on the training process for different network conditions and computing power of participating devices.The simulations address two architectures that exist for devices in federated learning and arrange devices to participate in training based on arithmetic power while achieving optimization of communication efficiency in the process of transferring model parameters.The results show that the methods we proposed can cope well with complex network situations,effectively balance the delay distribution of participating devices for local training,improve the communication eficiency during the transfer of model parameters,and improve the resource utilization in the network.
文摘An inclusion complex of podophyllotoxin (PPT) with 7-cyclodextrin (7-CD) was prepared. The behavior, char- acterization, and water solubility of the inclusion complex were carefully investigated via fluorescence spectroscopy, thermogravimetry, differential scanning calorimetry, X-ray diffraction analysis, and 1H and 2D nuclear magnetic resonance spectroscopy. Furthermore, antitumor activity to human cancer lines and toxicity in mice were studied. Results showed that the inclusion complex formed in a 1 : 1 ratio with a considerable apparent stability constant Ks (4245.5 Lomol-l). Water solubility was considerably improved. In addition, the anticancer activity of the inclusion complex was better than that of cis-platinum (DDP, positive control). Most importantly, the toxicity of podophyl- lotoxin inclusion complex reduced and became more safety to mice which will be great valuable to research its ap- plications as a kind of antitumor drug to human in the further.