WE say capitalism is notgood, but when it comes to discovering and using talents, it iscertainly very bold. It has a characteristic, which is taken for granted, that no priority is given to seniority, and that anyone ...WE say capitalism is notgood, but when it comes to discovering and using talents, it iscertainly very bold. It has a characteristic, which is taken for granted, that no priority is given to seniority, and that anyone suit-展开更多
The progress of modern industry has given rise to great requirements for network transmission latency and reliability in domains such as smart grid and intelligent driving.To address these challenges,the concept of Ti...The progress of modern industry has given rise to great requirements for network transmission latency and reliability in domains such as smart grid and intelligent driving.To address these challenges,the concept of Time-sensitive networking(TSN)is proposed by IEEE 802.1TSN working group.In order to achieve low latency,Cyclic queuing and forwarding(CQF)mechanism is introduced to schedule Timetriggered(TT)flows.In this paper,we construct a TSN model based on CQF and formulate the flow scheduling problem as an optimization problem aimed at maximizing the success rate of flow scheduling.The problem is tackled by a novel algorithm that makes full use of the characteristics and the relationship between the flows.Firstly,by K-means algorithm,the flows are initially partitioned into subsets based on their correlations.Subsequently,the flows within each subset are sorted by a new special criteria extracted from multiple features of flow.Finally,a flow offset selecting method based on load balance is used for resource mapping,so as to complete the process of flow scheduling.Experimental results demonstrate that the proposed algorithm exhibits significant advantages in terms of scheduling success rate and time efficiency.展开更多
联邦学习作为解决数据隔离问题的新兴范式,能够在不需要客户端上传原始数据的情况下训练全局模型,有效保护用户隐私。由于客户端数量众多但通信资源有限,只能选择部分客户端参与模型聚合。然而联邦学习系统存在设备异构和数据异质等挑战...联邦学习作为解决数据隔离问题的新兴范式,能够在不需要客户端上传原始数据的情况下训练全局模型,有效保护用户隐私。由于客户端数量众多但通信资源有限,只能选择部分客户端参与模型聚合。然而联邦学习系统存在设备异构和数据异质等挑战,简单的客户端选择策略无法考虑环境的动态特性,会拖慢模型的收敛速度,降低模型性能。考虑到客户端状态的时变,提出了全新的客户端可用性评估指标,建立了多重约束下的联邦学习客户端选择模型,建模为损失最小化问题;将优化问题转化为马尔可夫决策过程,提出了一种基于深度强化学习的联邦学习客户端自适应选择(Adaptive Selection for Clients in Federated Learning based on Deep Reinforcement Learning,ASC-DRL)算法,综合考虑通信延迟、资源消耗及客户端可用性,通过代理服务器与环境之间的持续交互最大化奖励函数,得到最优客户端选择方案。实验结果表明,提出的ASC-DRL算法相比于传统联邦学习算法,在模型精度和训练损失方面有着最高89.2%和99.8%的效果提升,能够有效适应动态环境变化,提升联邦学习整体性能和稳定性。展开更多
文摘WE say capitalism is notgood, but when it comes to discovering and using talents, it iscertainly very bold. It has a characteristic, which is taken for granted, that no priority is given to seniority, and that anyone suit-
基金supported by Science and Technology Project of State Grid Corporation Headquarters under Grant 5108-202218280A-2-170-XG(Development and Application of Power Time-Sensitive Network Switching Chip。
文摘The progress of modern industry has given rise to great requirements for network transmission latency and reliability in domains such as smart grid and intelligent driving.To address these challenges,the concept of Time-sensitive networking(TSN)is proposed by IEEE 802.1TSN working group.In order to achieve low latency,Cyclic queuing and forwarding(CQF)mechanism is introduced to schedule Timetriggered(TT)flows.In this paper,we construct a TSN model based on CQF and formulate the flow scheduling problem as an optimization problem aimed at maximizing the success rate of flow scheduling.The problem is tackled by a novel algorithm that makes full use of the characteristics and the relationship between the flows.Firstly,by K-means algorithm,the flows are initially partitioned into subsets based on their correlations.Subsequently,the flows within each subset are sorted by a new special criteria extracted from multiple features of flow.Finally,a flow offset selecting method based on load balance is used for resource mapping,so as to complete the process of flow scheduling.Experimental results demonstrate that the proposed algorithm exhibits significant advantages in terms of scheduling success rate and time efficiency.
文摘联邦学习作为解决数据隔离问题的新兴范式,能够在不需要客户端上传原始数据的情况下训练全局模型,有效保护用户隐私。由于客户端数量众多但通信资源有限,只能选择部分客户端参与模型聚合。然而联邦学习系统存在设备异构和数据异质等挑战,简单的客户端选择策略无法考虑环境的动态特性,会拖慢模型的收敛速度,降低模型性能。考虑到客户端状态的时变,提出了全新的客户端可用性评估指标,建立了多重约束下的联邦学习客户端选择模型,建模为损失最小化问题;将优化问题转化为马尔可夫决策过程,提出了一种基于深度强化学习的联邦学习客户端自适应选择(Adaptive Selection for Clients in Federated Learning based on Deep Reinforcement Learning,ASC-DRL)算法,综合考虑通信延迟、资源消耗及客户端可用性,通过代理服务器与环境之间的持续交互最大化奖励函数,得到最优客户端选择方案。实验结果表明,提出的ASC-DRL算法相比于传统联邦学习算法,在模型精度和训练损失方面有着最高89.2%和99.8%的效果提升,能够有效适应动态环境变化,提升联邦学习整体性能和稳定性。
