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Edge Federation is a new computing paradigm that seamlessly interconnects theresources of multiple edge service providers. A key challenge in such systemsis the deployment of latency-critical and AI based resource-intensiveapplications in constrained devices. To address this challenge, we propose anovel memory-efficient deep learning based model, namely generativeoptimization networks (GON). Unlike GANs, GONs use a single network to bothdiscriminate input and generate samples, significantly reducing their memoryfootprint. Leveraging the low memory footprint of GONs, we propose adecentralized fault-tolerance method called DRAGON that runs simulations (asper a digital modeling twin) to quickly predict and optimize the performance ofthe edge federation. Extensive experiments with real-world edge computingbenchmarks on multiple Raspberry-Pi based federated edge configurations showthat DRAGON can outperform the baseline methods in fault-detection and Qualityof Service (QoS) metrics. Specifically, the proposed method gives higher F1scores for fault-detection than the best deep learning (DL) method, whileconsuming lower memory than the heuristic methods. This allows for improvementin energy consumption, response time and service level agreement violations byup to 74, 63 and 82 percent, respectively.

DRAGON: Decentralized Fault Tolerance in Edge Federations