Abstract As a highly contagious livestock viral disease, foot-and-mouth disease poses a great threat to the beef-cattle industry. Direct animal movement is always considered as a major route for between-farm transmission of FMD virus. Sharing contaminated equipment and vehicles have also attracted increasing interests as an indirect but considerable route for FMD virus transmission. With the rapid development of communication technologies, information-sharing techniques have been used to control epidemics. In this paper, we built farm-level time-series three-layer networks to simulate the between-farm FMD virus transmission in southwest Kansas by cattle movements (direct-contact layer) and truck visits (indirect-contact layer) and evaluate the impact of information-sharing techniques (information-sharing layer) on mitigating the epidemic. Here, the information-sharing network is defined as the structure that enables the quarantine of farms that are connected with infected farms. When a farm is infected, its infection status is shared with the neighboring farms in the information-sharing network, which in turn become quarantined. The results show that truck visits can enlarge the epidemic size and prolong the epidemic duration of the FMD outbreak by cattle movements, and that the information-sharing technique is able to mitigate the epidemic. The mitigation effect of the information-sharing network varies with the information-sharing network topology and different participation levels. In general, an increased participation leads to a decreased epidemic size and an increased quarantine size. We compared the mitigation performance of three different information-sharing networks (random network, contact-based network, and distance-based network) and found the outbreak on the network with contact-based information-sharing layer has the smallest epidemic size under almost any participation level and smallest quarantine size with high participation. Furthermore, we explored the potential economic loss from the infection and the quarantine. By varying the ratio of the average loss of quarantine to the loss of infection, we found high participation results in reduced economic losses under the realistic assumption that culling costs are much greater than quarantine costs.
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RoADTrain: Route-Assisted Decentralized Peer Model Training among Connected Vehicles
Fully decentralized model training for on-road vehicles
can leverage crowdsourced data while not depending on
central servers, infrastructure or Internet coverage. However,
under unreliable wireless communication and short contact
duration, model sharing among peer vehicles may suffer severe
losses thus fail frequently. To address these challenges, we
propose “RoADTrain”, a route-assisted decentralized peer model
training approach that carefully chooses vehicles with high
chances of successful model sharing. It bounds the per round
communication time yet retains model performance under vehicle
mobility and unreliable communication. Based on shared route
information, a connected cluster of vehicles can estimate and
embed the link reliability and contact duration information into
the communication topology. We decompose the topology into
subgraphs supporting parallel communication, and identify a
subset of them with the highest algebraic connectivity that can
maximize the speed of the information flow in the cluster with
high model sharing successes, thus accelerating model training in
the cluster. We conduct extensive evaluation on driving decision
making models using the popular CARLA simulator. RoADTrain
achieves comparable driving success rates and 1.2−4.5× faster
convergence than representative decentralized learning methods
that always succeed in model sharing (e.g., SGP), and significantly
outperforms other benchmarks that consider losses by 17−27%
in the hardest driving conditions. These demonstrate that route
sharing enables shrewd selection of vehicles for model sharing,
thus better model performance and faster convergence against
wireless losses and mobility.
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- Award ID(s):
- 2007715
- NSF-PAR ID:
- 10462044
- Date Published:
- Journal Name:
- IEEE International Conference on Distributed Computing Systems (ICDCS)
- ISSN:
- 0743-166X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
