A Population Protocol for Uniform k-Partition Under Global Fairness

2018 ◽  
Author(s):  
Hiroto Yasumi ◽  
Naoki Kitamura ◽  
Fukuhito Ooshita ◽  
Taisuke Izumi ◽  
Michiko Inoue
Keyword(s):  
Population Protocol ◽  
Global Fairness

scholarly journals A Population Protocol for Uniform k-partition under Global Fairness

2019 ◽  
Vol 9 (1) ◽  
pp. 97-110 ◽  
Author(s):  
Hiroto Yasumi ◽  
Naoki Kitamura ◽  
Fukuhito Ooshita ◽  
Taisuke Izumi ◽  
Michiko Inoue
Keyword(s):  
Population Protocol ◽  
Global Fairness

Heterogeneous fairness algorithm based on federated learning in intelligent transportation system

2021 ◽  
pp. 1-9
Author(s):  
Yue Jiang ◽  
Gaochao Xu ◽  
Zhiyi Fang ◽  
Shinan Song ◽  
Bingbing Li
Keyword(s):  
Intelligent Transportation System ◽  
Influence Factor ◽  
Learning Algorithm ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Massive Data ◽  
Infrared Sensors ◽  
Distributed Sensors ◽  
Average Accuracy ◽  
Global Fairness

With the development of the Intelligent Transportation System, various distributed sensors (including GPS, radar, infrared sensors) process massive data and make decisions for emergencies. Federated learning is a new distributed machine learning paradigm, in which system heterogeneity is the difficulty of fairness design. This paper designs a system heterogeneous fair federated learning algorithm (SHFF). SHFF introduces the equipment influence factor I into the optimization target and dynamically adjusts the equipment proportion with other performance. By changing the global fairness parameter θ, the algorithm can control fairness according to the actual needs. Experimental results show that, compared with the popular q-FedAvg algorithm, the SHFF algorithm proposed in this paper improves the average accuracy of the Worst 10% by 26% and reduces the variance by 61%.

scholarly journals Leader Election Requires Logarithmic Time in Population Protocols

2020 ◽  
Vol 30 (01) ◽  
pp. 2050005 ◽  
Author(s):  
Yuichi Sudo ◽  
Toshimitsu Masuzawa
Keyword(s):  
Lower Bound ◽  
High Probability ◽  
Leader Election ◽  
Stabilization Time ◽  
Population Protocols ◽  
Population Protocol ◽  
Time Optimal ◽  
Exact Size

This paper shows that every leader election protocol requires logarithmic stabilization time both in expectation and with high probability in the population protocol model. This lower bound holds even if each agent has knowledge of the exact size of a population and is allowed to use an arbitrarily large number of agent states. This lower bound concludes that the protocol given in [Sudo et al., SSS 2019] is time-optimal in expectation.

scholarly journals Logarithmic Expected-Time Leader Election in Population Protocol Model

2019 ◽  
pp. 323-337 ◽  
Author(s):  
Yuichi Sudo ◽  
Fukuhito Ooshita ◽  
Taisuke Izumi ◽  
Hirotsugu Kakugawa ◽  
Toshimitsu Masuzawa
Keyword(s):  
Leader Election ◽  
Expected Time ◽  
Population Protocol

Adaptive proportional fair scheduling with global-fairness

2019 ◽  
Vol 25 (8) ◽  
pp. 5011-5025
Author(s):  
Zhao Li ◽  
Yujiao Bai ◽  
Jia Liu ◽  
Jie Chen ◽  
Zhixian Chang
Keyword(s):  
Proportional Fair ◽  
Fair Scheduling ◽  
Proportional Fair Scheduling ◽  
Global Fairness

scholarly journals Loosely-stabilizing leader election in a population protocol model

2012 ◽  
Vol 444 ◽  
pp. 100-112 ◽  
Author(s):  
Yuichi Sudo ◽  
Junya Nakamura ◽  
Yukiko Yamauchi ◽  
Fukuhito Ooshita ◽  
Hirotsugu Kakugawa ◽  
...  
Keyword(s):  
Leader Election ◽  
Population Protocol
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