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This research aims to enhance the testing of distributed cloud applications by employing federated deep Q-networks.

February 2, 2026Open Access

A Federated Deep Q-Network Approach for Distributed Cloud Testing: Methodology and Case Study

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This methodology improves testing efficiency in cloud environments, addressing challenges in IoT applications.

This research aims to enhance the testing of distributed cloud applications by employing federated deep Q-networks.
Proposed a federated deep Q-network approach incorporating local model training.
Utilized parameter updates to create a global model at a central server.
Replaced traditional coordination messages with learned decision functions for dynamic coordination.
Achieved approximately 58% reduction in coordination messages compared to traditional methods.
Demonstrated significant improvements in testing efficiency and effectiveness in dynamic environments.

Oualla et al. (2026) studied this question.

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February 2, 2026Open Access

View Full Paper

Ask AI

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Authors

This methodology improves testing efficiency in cloud environments, addressing challenges in IoT applications.

This research aims to enhance the testing of distributed cloud applications by employing federated deep Q-networks.
Proposed a federated deep Q-network approach incorporating local model training.
Utilized parameter updates to create a global model at a central server.
Replaced traditional coordination messages with learned decision functions for dynamic coordination.
Achieved approximately 58% reduction in coordination messages compared to traditional methods.
Demonstrated significant improvements in testing efficiency and effectiveness in dynamic environments.

Oualla et al. (2026) studied this question.

View Full Paper

Ask AI

Bookmark