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A notion of fc-robustness for complex networks has received much attention recently. The motivation for this notion of robustness is to measure the effectiveness of local-information-based diffusion algorithms in the presence of adversarial nodes. In this paper, we first correct the relationship between fc-robustness and fc-connectivity studied in related work. Then we derive a sharp zero-one law...
We present a distributed online learning scheme to classify data captured from distributed and dynamic data sources. Our scheme consists of multiple distributed local learners, which analyze different streams of data that are correlated to a common event that needs to be classified. Each learner uses a local classifier to make a local prediction. The local predictions are then collected by each learner...
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